Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis
DOI:
https://doi.org/10.1007/s10462-024-10814-2الكلمات المفتاحية:
Artificial intelligence ، Image processing ، Healthcare ، Medical image analysis ، machine learning ، Deep learning ، Preprocessing ، Segmentation ، Feature extraction ، Classificationالملخص
In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.
المراجع
Abbasloo, A., Wiens, V., Hermann, M., & Schultz, T. (2016). Visualizing tensor normal distributions at multiple levels of detail. IEEE Transactions on Visualization and Computer Graphics, 22(1), 975–984.
Abdar, M., Samami, M., Dehghani Mahmoodabad, S., Doan, T., Mazoure, B., Hashemifesharaki, R., ... & Nahavandi, S. (2021). Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Computers in Biology and Medicine, 135, Article 104418.
Abdou, M. A. (2022). Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Computing and Applications, 34(8), 5791–5812.
Abraham, B., & Nair, M. S. (2018). Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier. Biocybernetics and Biomedical Engineering, 38(3), 733–744.
Abraham, N. J., Daway, H. G., & Ali, R. A. (2022). Low lightness image enhancement using modified DCP based lightness mapping in lab color space. International Journal of Intelligent Engineering and Systems, 15(5), 244–251.
Acharya, U. K., & Kumar, S. (2021). Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement. Optik, 230, Article 166273.
Agarwal, M., & Mahajan, R. (2018). Medical image contrast enhancement using range limited weighted histogram equalization. Procedia Computer Science, 125, 149–156.
Agrawal, R., Sharma, M., & Singh, B. B. (2018). Segmentation of brain lesions in MRI and CT scan images: A hybrid approach using k-means clustering and image morphology. Journal of The Institution of Engineers (India): Series B, 99(2), 173–180.
Ahmed, A. (2020). Implementing relevance feedback for content-based medical image retrieval. IEEE Access, 8, 79969–79976.
Akakin, H. C., & Gurcan, M. N. (2012). Content-based microscopic image retrieval system for multi-image queries. IEEE Transactions on Information Technology in Biomedicine, 16(4), 758–769.
Alam, Z., Rahman, M. S., & Rahman, M. S. (2019). A random forest based predictor for medical data classification using feature ranking. Informatics in Medicine Unlocked, 15, Article 100180.
Almubarak, H., Bazi, Y., & Alajlan, N. (2020). Two-stage mask-RCNN approach for detecting and segmenting the optic nerve head, optic disc, and optic cup in fundus images. Applied Sciences, 10(11), Article 3833.
Alnaggar, O. A. M. F., Jagadale, B. N., & Narayan, S. H. (2022a). MRI brain tumor detection using boosted crossbred random forests and chimp optimization algorithm based convolutional neural networks. International Journal of Intelligent Engineering and Systems, 15(2), 36–46.
Alnaggar, O. A. M. F., Jagadale, B. N., Narayan, S. H., & Saif, M. A. N. (2022b). Brain tumor detection from 3D MRI using hyper-layer convolutional neural networks and hyper-heuristic extreme learning machine. Concurrency and Computation: Practice and Experience, 34(24), Article e7215.
Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019). Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging, 6(1), Article 014006.
Alqazzaz, S., Sun, X., Yang, X., & Nokes, L. (2019). Automated brain tumor segmentation on multi-modal MR image using SegNet. Computational Visual Media, 5(2), 209–219.
Alroobaea, R., Rubaiee, S., Bourouis, S., Bouguila, N., & Alsufyani, A. (2020). Bayesian inference framework for bounded generalized Gaussian-based mixture model and its application to biomedical images classification. International Journal of Imaging Systems and Technology, 30(1), 18–30.
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019). Going deep in medical image analysis: Concepts, methods, challenges, and future directions. IEEE Access, 7, 99540–99572.
Altun Güven, S., & Talu, M. F. (2023). Brain MRI high resolution image creation and segmentation with the new GAN method. Biomedical Signal Processing and Control, 80, Article 104246.
Amini, N., & Shalbaf, A. (2022). Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images. International Journal of Imaging Systems and Technology, 32(1), 102–110.
Anam, C., Adi, K., Sutanto, H., Arifin, Z., Budi, W. S., Fujibuchi, T., & Dougherty, G. (2020). Noise reduction in CT images using a selective mean filter. Journal of Biomedical Physics and Engineering, 10(5), 623–634.
Anoop, V., & Bipin, P. R. (2019). Medical image enhancement by a bilateral filter using optimization technique. Journal of Medical Systems, 43(8), Article 248.
Anshad, P. Y. M., Kumar, S. S., & Shahudheen, S. (2019). Segmentation of chondroblastoma from medical images using modified region growing algorithm. Cluster Computing, 22(S6), 13437–13444.
Arabahmadi, M., & Farahbakhsh, R. (2022). Deep learning for smart healthcare: A survey on brain tumor detection from medical imaging. Sensors, 22(15), 1–27.
Arabi, H., & Zaidi, H. (2021). Non-local mean denoising using multiple PET reconstructions. Annals of Nuclear Medicine, 35(2), 176–186.
Armato, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., ... & Clarke, L. P. (2011). The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38(2), 915–031.
Arslan, H., & Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Engineering Science and Technology, an International Journal, 24(4), 839–847.
Aruna Kumar, S. V., & Harish, B. S. (2018). A modified intuitionistic fuzzy clustering algorithm for medical image segmentation. Journal of Intelligent Systems, 27(4), 593–607.
Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., ... & Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific Reports, 8(1), Article 12054.
Aswiga, R. V., Aishwarya, R., & Shanthi, A. P. (2021). Augmenting transfer learning with feature extraction techniques for limited breast imaging datasets. Journal of Digital Imaging, 34(3), 618–629.
Awan, R., & Rajpoot, N. (2018). Deep autoencoder features for registration of histology images. In International Conference on Communications in Computer and Information Science (Vol. 894, pp. 371–378). Springer.
Azad, R., Heidari, M., Shariatnia, M., Aghdam, E. K., Karimijafarbigloo, S., Adeli, E., & Merhof, D. (2022). TransDeepLab: Convolution-free transformer-based DeepLab v3+ for medical image segmentation. In Predictive Intelligence in Medicine (pp. 91–102). Springer.
Babenko, V., Nastenko, I., Pavlov, V., Horodetska, O., Dykan, I., Tarasiuk, B., & Lazoryshinets, V. (2023). Classification of pathologies on medical images using the algorithm of random forest of optimal-complexity trees. Cybernetics and Systems Analysis, 59(2), 190–202.
Bafna, Y., Verma, K., Panigrahi, L., & Sahu, S. P. (2018). Automated boundary detection of breast cancer in ultrasound images using watershed algorithm. Advances in Intelligent Systems and Computing, 696, 729–738.
Bai, B., Liu, P.-Z., Du, Y.-Z., & Luo, Y.-M. (2018). Automatic segmentation of cervical region in colposcopic images using K-means. Australasian Physical & Engineering Sciences in Medicine, 41(4), 1077–1085.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., ... & Davatzikos, C. (2017). Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4, Article 170117.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., ... & Menze, B. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.
Balaji, V. R., Suganthi, T. S., Rajadevi, R., Krishna Kumar, V., Saravana Balaji, B., & Pandiyan, S. (2020). Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement, 163, Article 107922.
Balasamy, K., & Shamia, D. (2021). Feature extraction-based medical image watermarking using fuzzy-based median filter. IETE Journal of Research, 69(1), 1–9.
Baldeon Calisto, M., & Lai-Yuen, S. K. (2020). AdaEn-Net: An ensemble of adaptive 2D–3D fully convolutional networks for medical image segmentation. Neural Networks, 126, 76–94.
Baranwal, S. K., Jaiswal, K., Vaibhav, K., Kumar, A., & Srikantaswamy, R. (2020). Performance analysis of brain tumour image classification using CNN and SVM. In Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 537–542). IEEE.
Barshooi, H. A., & Amirkhani, A. (2022). A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-ray images. Biomedical Signal Processing and Control, 72, Article 103326.
Baselice, F., Ferraioli, G., Ambrosanio, M., Pascazio, V., & Schirinzi, G. (2018). Enhanced Wiener filter for ultrasound image restoration. Computer Methods and Programs in Biomedicine, 153, 71–81.
Bautista, P. A., Hashimoto, N., & Yagi, Y. (2014). Color standardization in whole slide imaging using a color calibration slide. Journal of Pathology Informatics, 5(1), Article 4.
Benhassine, N. E., Boukaache, A., & Boudjehem, D. (2021). Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet. International Journal of Imaging Systems and Technology, 31(4), 1906–1920.
Bhavani, R. R., & Jiji, G. W. (2018). Image registration for varicose ulcer classification using KNN classifier. International Journal of Computers and Applications, 40(2), 88–97.
Bi, L., Feng, D., & Kim, J. (2018). Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. The Visual Computer, 34(6), 1043–1052.
Biratu, E. S., Schwenker, F., Debelee, T. G., Kebede, R. S., Negera, W. G., & Molla, H. T. (2021). Enhanced region growing for brain tumor MR image segmentation. Journal of Imaging, 7(2), 1–19.
Biswas, A., & Islam, M. S. (2021). Brain tumor types classification using k-means clustering and ANN approach. In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 654–658). IEEE.
Blotta, E., Bouchet, A., Ballarin, V., & Pastore, J. (2011). Enhancement of medical images in HSI color space. Journal of Physics: Conference Series, 332(1), Article 012041.
Bonny, S., Chanu, Y. J., & Singh, K. M. (2019). Speckle reduction of ultrasound medical images using Bhattacharyya distance in modified non-local mean filter. Signal, Image and Video Processing, 13(2), 299–305.
Bouaziz, A., Draa, A., & Chikhi, S. (2015). Artificial bees for multilevel thresholding of iris images. Swarm and Evolutionary Computation, 21, 32–40.
Braiki, M., Benzinou, A., Nasreddine, K., & Hymery, N. (2020). Automatic human dendritic cells segmentation using K-means clustering and Chan-Vese active contour model. Computer Methods and Programs in Biomedicine, 195, Article 105520.
Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., ... & Utikal, J. S. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119, 11–17.
Cabeza-Gil, I., Ruggeri, M., Chang, Y.-C., Calvo, B., & Manns, F. (2022). Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks. Biomedical Optics Express, 13(5), 2810–2823.
Cai, W., Zhai, B., Liu, Y., Liu, R., & Ning, X. (2021). Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation. Displays, 70, Article 102106.
Çalışkan, A. (2017). Three-dimensional modeling in medical image processing by using fractal geometry. Journal of Computers, 12(5), 479–485.
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., ... & Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(8), 1301–1309.
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023). Swin-Unet: Unet-like pure transformer for medical image segmentation. In Machine Learning in Medical Imaging (pp. 205–218). Springer.
Cao, W., Zheng, J., Xiang, D., Ding, S., Sun, H., Yang, X., ... & Dai, Y. (2021). Edge and neighborhood guidance network for 2D medical image segmentation. Biomedical Signal Processing and Control, 69, Article 102856.
Capor Hrosik, R., Tuba, E., Dolicanin, E., Jovanovic, R., & Tuba, M. (2019). Brain image segmentation based on firefly algorithm combined with K-means clustering. Studies in Informatics and Control, 28(2), 167–176.
Çelik, Y., & Karabatak, M. (2023). Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders. Expert Systems, 40(4), Article e12819.
Chakraborty, D., Zhuang, Z., Xue, H., Fiecas, M. B., Shen, X., & Pan, W. (2023). Deep learning-based feature extraction with MRI data in neuroimaging genetics for Alzheimer’s disease. Genes, 14(3), Article 626.
Chakraborty, S., Paul, S., & Hasan, K. M. A. (2022). A transfer learning-based approach with deep CNN for COVID-19- and pneumonia-affected chest X-ray image classification. SN Computer Science, 3(1), Article 17.
Chanu, Q. C., & Singh, K. M. (2018). Impulse noise removal from medical images by two stage quaternion vector median filter. Journal of Medical Systems, 42(10), Article 1057.
Chen, B. Q., Chen, C., Ge, J., Xu, Q., Shu, T., & Liu, H. L. (2019). Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image. Journal of Central South University, 26(1), 120–131.
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
Chen, K., Chen, Z., Tai, Y., Peng, J., Shi, J., & Xia, C. (2018). A system design for virtual reality visualization of medical image. In 2018 26th International Conference on Geoinformatics (pp. 1–5). IEEE.
Chen, R. J., Lu, My., Shaban, M., Chen, C., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021). Whole slide images are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks. In Medical Image Computing and Computer Assisted Intervention (pp. 339–349). Springer.
Chen, Z., Chen, Z., Liu, J., Zheng, Q., Zhu, Y., Zuo, Y., ... & Li, Y. (2021). Weakly supervised histopathology image segmentation with sparse point annotations. IEEE Journal of Biomedical and Health Informatics, 25(5), 1673–1685.
Chen, Z., Zhou, Z., & Adnan, S. (2021). Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising. Medical & Biological Engineering & Computing, 59(3), 607–620.
Cheng, J., Mo, X., Wang, X., Parwani, A., Feng, Q., & Huang, K. (2018). Identification of topological features in renal tumor microenvironment associated with patient survival. Bioinformatics, 34(6), 1024–1030.
Cheng, S., Liu, S., Yu, J., Rao, G., Xiao, Y., Han, W., ... & Liu, X. (2021). Robust whole slide image analysis for cervical cancer screening using deep learning. Nature Communications, 12(1), Article 5639.
Cheng, Z., & Wang, J. (2020). Improved region growing method for image segmentation of three-phase materials. Powder Technology, 368, 80–89.
Chervyakov, N., Lyakhov, P., & Nagornov, N. (2020). Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging. Applied Sciences, 10(4), Article 1223.
Chowdhury, A. R., Chatterjee, T., & Banerjee, S. (2019). A random forest classifier-based approach in the detection of abnormalities in the retina. Medical & Biological Engineering & Computing, 57(1), 193–203.
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., ... & Halpern, A. (2018). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI). In 2018 IEEE 15th International Symposium on Biomedical Imaging (pp. 168–172). IEEE.
Combalia, M., Codella, N. C. F., Rotemberg, V., Helba, B., Vilaplana, V., Reiter, O., ... & Malvehy, J. (2019). BCN20000: Dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288.
Cui, S., Shen, X., & Lyu, Y. (2019). Automatic segmentation of brain tumor image based on region growing with co-constraint. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 603–615). Springer.
Cui, Y., Zhang, G., Liu, Z., Xiong, Z., & Hu, J. (2019). A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Medical & Biological Engineering & Computing, 57(9), 2027–2043.
Das, A., Acharya, U. R., Panda, S. S., & Sabut, S. (2019). Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cognitive Systems Research, 54, 165–175.
Dash, S., Parida, P., & Mohanty, J. R. (2023). Illumination robust deep convolutional neural network for medical image classification. Soft Computing, 27, 1–15.
Dayananda, C., Choi, J. Y., & Lee, B. (2022). A Squeeze U-SegNet architecture based on residual convolution for brain MRI segmentation. IEEE Access, 10密, 52804–52817.
Deepa, V., Sathish Kumar, C., & Cherian, T. (2022). Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by Siamese network. Physical and Engineering Sciences in Medicine, 45(2), 623–635.
Deepak, S., & Ameer, P. M. (2021). Automated categorization of brain tumor from MRI using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8357–8369.
Dhivyaa, C. R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., & Johnpaul, P. (2020). Skin lesion classification using decision trees and random forest algorithms. Journal of Ambient Intelligence and Humanized Computing, 11, 1–12.
Dinh, P. H., & Giang, N. L. (2022). A new medical image enhancement algorithm using adaptive parameters. International Journal of Imaging Systems and Technology, 32(6), 2198–2218.
Dodington, D. W., Lagree, A., Tabbarah, S., Mohebpour, M., Sadeghi-Naini, A., Tran, W. T., & Lu, F.-I. (2021). Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Research and Treatment, 186(2), 379–389.
Dongyao, J., Zhengyi, L., & Chuanwang, Z. (2020). Detection of cervical cancer cells based on strong feature CNN-SVM network. Neurocomputing, 411, 112–127.
dos Santos, J. C. M., Carrijo, G. A., de Fátima dos Santos Cardoso, C., Ferreira, J. C., Sousa, P. M., & Patrocínio, A. C. (2020). Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Research on Biomedical Engineering, 36(2), 107–119.
Ekong, F., Yu, Y., Patamia, R. A., Feng, X., Tang, Q., Mazumder, P., & Cai, J. (2022). Bayesian depth-wise convolutional neural network design for brain tumor MRI classification. Diagnostics, 12(7), Article 1657.
Elaiyaraja, G., Kumaratharan, N., & Chandra Sekhar Rao, T. (2022). Fast and efficient filter using wavelet threshold for removal of Gaussian noise from MRI/CT scanned medical images/color video sequence. IETE Journal of Research, 68(1), 10–22.
Elhoseny, M., & Shankar, K. (2019). Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements. Measurement, 143, 125–135.
Enguehard, J., O’Halloran, P., & Gholipour, A. (2019). Semi-supervised learning with deep embedded clustering for image classification and segmentation. IEEE Access, 7, 11093–11104.
Fan, X., Sun, Z., Tian, E., Yin, Z., & Cao, G. (2023). Medical image contrast enhancement based on improved sparrow search algorithm. International Journal of Imaging Systems and Technology, 33(1), 389–402.
Fang, L., Wang, X., & Wang, L. (2020). Multi-modal medical image segmentation based on vector-valued active contour models. Information Sciences, 513, 504–518.
Faragallah, O. S., El-Hoseny, H. M., & El-sayed, H. S. (2023). Efficient brain tumor segmentation using OTSU and K-means clustering in homomorphic transform. Biomedical Signal Processing and Control, 84, Article 104712.
Faust, K., Xie, Q., Han, D., Goyle, K., Volynskaya, Z., Djuric, U., & Diamandis, P. (2018). Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinformatics, 19(1), Article 173.
Felfeliyan, B., Hareendranathan, A., Kuntze, G., Cornell, D., Forkert, N. D., Jaremko, J. L., & Ronsky, J. L. (2022). Self-supervised-RCNN for medical image segmentation with limited data annotation. arXiv preprint arXiv:2207.11191.
Feng, P., Lin, Y., & Lo, C. (2018). A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Medical Physics, 45(12), 5509–5514.
Feng, Y., Liu, Y., Liu, Z., Liu, W., Yao, Q., & Zhang, X. (2023). A novel interval iterative multi-thresholding algorithm based on hybrid spatial filter and region growing for medical brain MR images. Applied Sciences, 13(2), Article 1087.
Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(5), 826–834.
Ganaye, P., Sdika, M., & Benoit-Cattin, H. (2018). Towards integrating spatial localization in convolutional neural networks for brain image segmentation. In 2018 IEEE 15th International Symposium on Biomedical Imaging (pp. 621–625). IEEE.
Gao, Z., Lu, Z., Wang, J., Ying, S., & Shi, J. (2022). A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images. IEEE Journal of Biomedical and Health Informatics, 26(7), 3163–3173.
Gerrits, T., Rössl, C., & Theisel, H. (2019). Towards glyphs for uncertain symmetric second-order tensors. Computer Graphics Forum, 38(3), 325–336.
Gillmann, C., Wischgoll, T., Hamann, B., & Ahrens, J. (2018). Modeling and visualization of uncertainty-aware geometry using multi-variate normal distributions. In 2018 IEEE Pacific Visualization Symposium (PacificVis) (pp. 106–110). IEEE.
Goswami, T., Agarwal, A., & Chillarige, R. R. (2021). Multi-faceted hierarchical image segmentation taxonomy (MFHIST). IEEE Access, 9, 33543–33556.
Gour, M., Jain, S., & Sunil Kumar, T. (2020). Residual learning based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), 621–635.
Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., ... & Liu, J. (2019). CE-Net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 38(10), 2281–2292.
Guo, S., Wang, G., Han, L., Song, X., & Yang, W. (2022). COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter. Biomedical Signal Processing and Control, 75, Article 103552.
Gupta, D., & Ahmad, M. (2018). Brain MR image denoising based on wavelet transform. International Journal of Advanced Technology and Engineering Exploration, 5(38), 11–16.
Gupta, M., Taneja, H., & Chand, L. (2018a). Performance enhancement and analysis of filters in ultrasound image denoising. Procedia Computer Science, 132, 643–652.
Gupta, N., Bhatele, P., & Khanna, P. (2018b). Identification of gliomas from brain MRI through adaptive segmentation and run length of centralized patterns. Journal of Computer Science, 25, 213–220.
Gupta, R. K., Bharti, S., Kunhare, N., Sahu, Y., & Pathik, N. (2022). Brain tumor detection and classification using cycle generative adversarial networks. Interdisciplinary Sciences: Computational Life Sciences, 14(2), 485–502.
Habeeb, N. J. (2021). Performance enhancement of medical image fusion based on DWT and sharpening Wiener filter. Jordanian Journal of Computers and Information Technology, 7(2), 118–129.
Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate classification of COVID-19 based on incomplete heterogeneous data using a KNN variant algorithm. Arabian Journal for Science and Engineering, 46(9), 8261–8272.
Han, Q., Wang, H., Hou, M., Weng, T., Pei, Y., Li, Z., ... & Qiu, Z. (2023). HWA-SegNet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment. Computers in Biology and Medicine, 152, Article 106343.
Han, Z., Wei, B., Mercado, A., Leung, S., & Li, S. (2018). Spine-GAN: Semantic segmentation of multiple spinal structures. Medical Image Analysis, 50, 23–35.
Hannah Inbarani, H., Azar, T. A., & Jothi, G. (2020). Leukemia image segmentation using a hybrid histogram-based soft covering rough K-means clustering algorithm. Electronics, 9(1), 1–22.
Hardas, M., Mathur, S., Bhaskar, A., & Kalla, M. (2022). Retinal fundus image classification for diabetic retinopathy using SVM predictions. Physical and Engineering Sciences in Medicine, 45(3), 781–791.
Hashemi, S. R., Salehi, S. S. M., Erdogmus, D., Prabhu, S. P., Warfield, S. K., & Gholipour, A. (2019). Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection. IEEE Access, 7, 1721–1735.
He, Y., Zheng, Y., Zhao, Y., Ren, Y., Lian, J., & Gee, J. (2017). Retinal image denoising via bilateral filter with a spatial kernel of optimally oriented line spread function. Computational and Mathematical Methods in Medicine, 2017, Article 1769834.
Homeyer, A., Schenk, A., Arlt, J., Dahmen, U., Dirsch, O., & Hahn, H. H. (2013). Practical quantification of necrosis in histological whole-slide images. Computerized Medical Imaging and Graphics, 37(4), 313–322.
Hooda, H., & Verma, O. P. (2022). Fuzzy clustering using gravitational search algorithm for brain image segmentation. Multimedia Tools and Applications, 81(20), 29633–29652.
Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203–210.
Hu, X., Luo, W., Hu, J., Guo, S., Huang, W., Scott, M. R., ... & Reyes, M. (2020). Brain SegNet: 3D local refinement network for brain lesion segmentation. BMC Medical Imaging, 20(1), Article 17.
Huang, H., Meng, F., Zhou, S., Jiang, F., & Manogaran, G. (2019). Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access, 7, 12386–12396.
Ibrahem Alhayali, R. A., Ahmed, M. A., Mohialden, Y. M., & Ali, H. H. (2020). Efficient method for breast cancer classification based on ensemble Hoeffding tree and Naïve Bayes. Indonesian Journal of Electrical Engineering and Computer Science, 18(2), 1074–1080.
Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74–87.
Ignacio, B. S. E., Villaflor, L. M. B., Chiong, V. A., & Peña, C. F. (2022). A performance evaluation of YOLOv3 and CIE Lab color space pixel color analysis in fire detection. In 2022 2nd International Conference in Information and Computing Research (iCORE) (pp. 279–284). IEEE.
Iizuka, O., Kanavati, F., Kato, K., Rambeau, M., Arihiro, K., & Tsuneki, M. (2020). Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific Reports, 10(1), Article 1504.
Ilayarajaa, K. T., & Logashanmugam, E. (2020). Retinal blood vessel segmentation using morphological and Canny edge detection technique. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1–5). IEEE.
Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P. F., Kohl, S., ... & Maier-Hein, K. H. (2018). Abstract: nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. In Bildverarbeitung für die Medizin (pp. 22–22). Springer.
Jackins, V., Vimal, S., Kaliappan, M., & Lee, M. Y. (2021). AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. The Journal of Supercomputing, 77(5), 5198–5219.
Jeyaraj, P. R., & Samuel Nadar, E. R. (2019). Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. Journal of Cancer Research and Clinical Oncology, 145(4), 829–837.
Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020). DoubleU-Net: A deep convolutional neural network for medical image segmentation. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) (pp. 558–564). IEEE.
Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., Lange, T. D., Halvorsen, P., & Johansen, H. D. (2019). ResUNet++: An advanced architecture for medical image segmentation. In 2019 IEEE International Symposium on Multimedia (ISM) (pp. 225–2255). IEEE.
Jiang, Y., Chen, L., Zhang, H., & Xiao, X. (2019a). Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE, 14(3), Article e0214587.
Jiang, Y., Gu, X., Wu, D., Hang, W., Xue, J., Qiu, S., & Chin-Teng, L. (2020). A novel negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space and its application to brain CT image segmentation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(1), 1–11.
Jiang, Y., Zhao, K., Xia, K., Xue, J., Zhou, L., Ding, Y., & Qian, P. (2019b). A novel distributed multitask fuzzy clustering algorithm for automatic MR brain image segmentation. Journal of Medical Systems, 43(5), Article 118.
Jiménez del Toro, O., Atzori, M., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., ... & Müller, H. (2017). Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. Medical Imaging 2017: Digital Pathology, 10140, Article 101400O.
Jung, Y. (2021). Automatic transfer function design for medical direct volume rendering via clustering-based ray analysis. Journal of Medical Imaging and Health Informatics, 11(4), 1055–1062.
Kalyani, R., Sathya, P. D., & Sakthivel, V. P. (2021). Multilevel thresholding for medical image segmentation using teaching-learning based optimization algorithm. International Journal of Intelligent Engineering and Systems, 14(2), 11–21.
Kanavati, F., Ichihara, S., & Tsuneki, M. (2022). A deep learning model for breast ductal carcinoma in situ classification in whole slide images. Virchows Archiv, 480(5), 1009–1022.
Kaplan, K., Kaya, Y., Kuncan, M., & Ertunç, H. M. (2020). Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical Hypotheses, 139, Article 109696.
Karthikamani, R., & Rajaguru, H. (2022). Detection of liver abnormalities—a new paradigm in medical image processing and classification techniques. International Journal of Imaging Systems and Technology, 32(6), 2219–2239.
Kaur, T., & Gandhi, T. K. (2020). Deep convolutional neural networks with transfer learning for automated brain image classification. Machine Vision and Applications, 31(3), Article 20.
Kawahara, D., Tsuneda, M., Ozawa, S., Okamoto, H., Nakamura, M., Nishio, T., & Nagata, Y. (2022). Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients. Journal of Applied Clinical Medical Physics, 23(5), 1–13.
Khagi, B., & Kwon, G. R. (2018). Pixel-label-based segmentation of cross-sectional brain MRI using simplified SegNet architecture-based CNN. Journal of Healthcare Engineering, 2018, Article 3640705.
Khairandish, M. O., Sharma, M., Jain, V., Chatterjee, J. M., & Jhanjhi, N. Z. (2022). A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM, 43(4), 290–299.
Khaled, A., Han, J. J., & Ghaleb, T. A. (2022). Multi-model medical image segmentation using multi-stage generative adversarial networks. IEEE Access, 10, 28590–28599.
Khan, H. A., Gong, X., Bi, F., & Ali, R. (2023). Novel light convolutional neural network for COVID detection with watershed based region growing segmentation. Journal of Imaging, 9(2), Article 42.
Khan, S., Khan, A., Maqsood, M., Aadil, F., & Ghazanfar, M. A. (2019). Optimized Gabor feature extraction for mass classification using cuckoo search for big data E-healthcare. Journal of Grid Computing, 17(2), 239–254.
Khawatmi, M., Steux, Y., Zourob, S., & Sailem, H. Z. (2022). ShapoGraphy: A user-friendly web application for creating bespoke and intuitive visualisation of biomedical data. Frontiers in Bioinformatics, 2, 1–11.
Khorram, B., & Yazdi, M. (2019). A new optimized thresholding method using ant colony algorithm for MR brain image segmentation. Journal of Digital Imaging, 32(1), 162–174.
Kim, S., Jang, Y., & Kim, S.-E. (2021). Image-based TF colorization with CNN for direct volume rendering. IEEE Access, 9, 124281–124294.
Korkmaz, S. A., & Binol, H. (2018). Analysis of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection. Journal of Molecular Structure, 1155, 312–322.
Korotkova, O., Salem, M., Dogariu, A., & Wolf, E. (2005). Changes in the polarization ellipse of random electromagnetic beams propagating through the turbulent atmosphere. Waves in Random and Complex Media, 15(3), 353–364.
Krishnakumar, S., & Manivannan, K. (2021). Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6751–6760.
Kshatri, S. S., & Singh, D. (2023). Convolutional neural network in medical image analysis: A review. Archives of Computational Methods in Engineering, 30, 1–15.
Kucharski, A., & Fabijańska, A. (2021). CNN-watershed: A watershed transform with predicted markers for corneal endothelium image segmentation. Biomedical Signal Processing and Control, 68, Article 102805.
Kumar, D. M., Satyanarayana, D., & Prasad, M. N. G. (2021). An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation. Multimedia Tools and Applications, 80(5), 6939–6957.
Kumar, G., & Bhatia, P. K. (2014). A detailed review of feature extraction in image processing systems. International Conference on Advanced Computing and Communication Technologies, 4, 5–12.
Kumar, N., Uppala, P., Duddu, K., Sreedhar, H., Varma, V., Guzman, G., ... & Sethi, A. (2018). Hyperspectral tissue image segmentation using semi-supervised NMF and hierarchical clustering. IEEE Transactions on Medical Imaging, 38(5), 1304–1313.
Kuo, C. F. J., & Wu, H. C. (2019). Gaussian probability bi-histogram equalization for enhancement of the pathological features in medical images. International Journal of Imaging Systems and Technology, 29(2), 132–145.
Kwon, Y., Won, JH., Kim, B. J., & Paik, M. C. (2020). Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Computational Statistics & Data Analysis, 142, Article 106816.
Lan, R., Zhong, S., Liu, Z., Shi, Z., & Luo, X. (2018). A simple texture feature for retrieval of medical images. Multimedia Tools and Applications, 77(9), 10853–10866.
Laouamer, L. (2022). New informed non-blind medical image watermarking based on local binary pattern. Traitement du Signal, 39(5), 1851–1856.
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Leo, L. M., Yogalakshmi, S., Simla, J. A., Prabhu, R. T., & Yokesh, V. (2021). Neural foraminal stenosis classifications using multi-feature hierarchical clustering and delineation. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 162–165.
Li, B., Tian, L., & Ou, S. (2010). An optical model for translucent volume rendering and its implementation using the preintegrated shear-warp algorithm. International Journal of Biomedical Imaging, 2010, 1–11.
Li, H., Li, A., & Wang, M. (2019). A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Computers in Biology and Medicine, 108, 150–160.
Li, H., Zhao, X., Su, A., Zhang, H., Liu, J., & Gu, G. (2020). Color space transformation and multi-class weighted loss for adhesive white blood cell segmentation. IEEE Access, 8密, 24808–24818.
Li, J., Shi, J., Chen, J., Du, Z., & Huang, L. (2023). Self-attention random forest for breast cancer image classification. Frontiers in Oncology, 13, 1–14.
Li, Z., Zhang, J., & Yang, X. (2013). Study and realization of multidimensional visualization techniques for multimodality medical images. Chinese Journal of Medical Instrumentation, 37(2), 100–102.
Liang, G., Hong, H., Xie, W., & Zheng, L. (2018). Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access, 6, 36188–36197.
Licciardo, G. D., Cappetta, C., & Di Benedetto, L. (2018). Design of a Gabor filter HW accelerator for applications in medical imaging. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(7), 1187–1194.
Liebgott, A., Küstner, T., Strohmeier, H., Hepp, T., Mangold, P., Martirosian, P., ... & Gatidis, S. (2018). ImFEATbox: A toolbox for extraction and analysis of medical image features. International Journal of Computer Assisted Radiology and Surgery, 13(12), 1881–1893.
Lin, A., Chen, B., Xu, J., Zhang, Z., Lu, G., & Zhang, D. (2022). DS-TransUNet: Dual swin transformer U-Net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement, 71, 1–15.
Liu, C., Liu, W., & Xing, W. (2019a). A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation. Journal of Visual Communication and Image Representation, 59, 89–107.
Liu, H., Wang, H., Wu, Y., & Xing, L. (2020). Superpixel region merging based on deep network for medical image segmentation. ACM Transactions on Intelligent Systems and Technology, 11(4), 1–22.
Liu, H., Xu, C., Feng, B., & Li, K. (2021). Multi-color space medical endoscope image enhancement method. In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) (pp. 223–228). IEEE.
Liu, M., Dong, J., Dong, X., Yu, H., & Qi, L. (2018). Segmentation of lung nodule in CT images based on Mask R-CNN. 2018 9th International Conference on Awareness Science and Technology (ICAST), 1–6.
Liu, X., Guo, S., Zhang, H., He, K., Mu, S., Guo, Y., & Li, X. (2019b). Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network. Medical Physics, 46(8), 3532–3542.
Liu, Z., Song, Y. Q., Sheng, V. S., Wang, L., Jiang, R., Zhang, X., & Yuan, D. (2019c). Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Systems with Applications, 126, 54–63.
Lomacenkova, A., & Arandjelovic, O. (2021). Whole slide pathology image patch based deep classification: An investigation of the effects of the latent autoencoder representation and the loss function form. IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 1–4.
Lu, C., & Mandal, M. (2015). Automated analysis and diagnosis of skin melanoma on whole slide histopathological images. Pattern Recognition, 48(8), 2738–2750.
Lucknavalai, K., & Schulze, J. P. (2020). Real-time contrast enhancement for 3D medical images using histogram equalization. In Advances in Visual Computing (pp. 224–235). Springer.
Luo, Y., Ma, Y., O’Brien, H., Jiang, K., Kohli, V., Maidelin, S., ... & Rhode, K. S. (2022). Edge-enhancement DenseNet for X-ray fluoroscopy image denoising in cardiac electrophysiology procedures. Medical Physics, 49(2), 1262–1275.
Luo, Y., Pan, J., & Fan, S. (2020). Retinal image classification by self-supervised fuzzy clustering network. IEEE Access, 8密, 1–10.
Ma, B., Ban, X., Huang, H., Chen, Y., Liu, W., & Zhi, Y. (2018). Deep learning-based image segmentation for Al-La alloy microscopic images. Symmetry, 10(4), 1–13.
Ma, F., Sun, T., Liu, L., & Jing, H. (2020a). Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network. Future Generation Computer Systems, 111, 17–26.
Ma, J., Chen, J., Chen, L., Jin, L., & Qin, X. (2020b). Dynamic visualization of uncertainties in medical feature of interest. IEEE Access, 8, 119170–119183.
Madaan, V., Roy, A., Gupta, C., Agrawal, P., Sharma, A., Bologa, C., & Prodan, R. (2021). XCOVNet: Chest X-ray image classification for COVID-19 early detection using convolutional neural networks. New Generation Computing, 39(3-4), 583–597.
Madhu, & Kumar, R. (2022). A hybrid feature extraction technique for content based medical image retrieval using segmentation and clustering techniques. Multimedia Tools and Applications, 81(6), 8345–8360.
Malik, S., Akram, T., Ashraf, I., Rafiullah, M., Ullah, M., & Tanveer, J. (2022). A hybrid preprocessor DE-ABC for efficient skin-lesion segmentation with improved contrast. Diagnostics, 12(11), 1–14.
Mall, P. K., Singh, P. K., & Yadav, D. (2019). GLCM based feature extraction and medical X-ray image classification using machine learning techniques. 2019 IEEE Conference on Information and Communication Technology (CICT), 1–6.
Mandyartha, E. P., Anggraeny, F. T., Muttaqin, F., & Akbar, F. A. (2020). Global and adaptive thresholding technique for white blood cell image segmentation. Journal of Physics: Conference Series, 1569(2), Article 022054.
Manoharan, H., Rambola, R. K., Kshirsagar, P. R., Chakrabarti, P., Alqahtani, J., Naveed, Q. N., ... & Mekuriyaw, W. D. (2022). Aerial separation and receiver arrangements on identifying lung syndromes using the artificial neural network. Journal of Healthcare Engineering, 2022, Article 4270776.
Mansour, N. A., Saleh, A. I., Badawy, M., & Ali, H. A. (2022). Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy. Journal of Ambient Intelligence and Humanized Computing, 13(1), 125–140.
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507.
Mateen, M., Wen, J., Nasrullah, S. S., & Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), Article 1.
Mohammed, M. A., et al. (2020). Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. The Journal of Supercomputing, 76(2), 1086–1104.
Mendonca, T., Ferreira, PM., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013). PH2—A dermoscopic image database for research and benchmarking. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, 5437–5440.
Meng, L., Tian, Y., & Bu, S. (2020). Liver tumor segmentation based on 3D convolutional neural network with dual scale. Journal of Applied Clinical Medical Physics, 21(1), 144–157.
Menze, B. H., Jakab, A. B. S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., ... & Leemput, K. (2015). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024.
Mercan, E., Aksoy, S., Shapiro, L. G., Weaver, D. L., Brunyé, T. T., & Elmore, J. G. (2016). Localization of diagnostically relevant regions of interest in whole slide images: A comparative study. Journal of Digital Imaging, 29(4), 496–506.
Mikołajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. International Interdisciplinary PhD Workshop (IIPhDW), 2018, 117–122.
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), 565–571.
Mittal, H., Chandra, A., Raju, P., & Ashish, P. (2021). A new clustering method for the diagnosis of CoVID19 using medical images. Complex & Intelligent Systems, 7(6), 2988–3011.
Mohd Sagheer, S. V., & George, S. N. (2020). A review on medical image denoising algorithms. Biomedical Signal Processing and Control, 61密, Article 102036.
Mohite, N. B., & Gonde, B. A. (2022). Deep features based medical image retrieval. Multimedia Tools and Applications, 81(8), 11379–11392.
Mondal, A. K., Dolz, J., & Desrosiers, C. (2018). Few-shot 3D multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241.
Monteiro, M., Newcombe, V. F. J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., ... & Glocker, B. (2020). Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: An algorithm development and multicentre validation study. The Lancet Digital Health, 2(6), e314–e322.
Mukherjee, L., Bui, H. D., Keikhosravi, A., Loeffler, A., & Eliceiri, K. W. (2019). Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images. Journal of Biomedical Optics, 24(12), Article 126003.
Naimi, H. (2022). Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter. Australian Journal of Electrical and Electronics Engineering, 19(3), 294–299.
Napte, K. M., & Mahajan, A. (2023). Liver segmentation using marker controlled watershed transform. International Journal of Electrical and Computer Engineering, 13(2), 1541–1549.
Narayan, V., Mall, P. K., Awasthi, S., Srivastava, S., & Gupta, A. (2023). FuzzyNet: Medical image classification based on GLCM texture feature. International Conference on Artificial Intelligence and Smart Communication (AISC), 769–773.
Narayana, P. A., Coronado, I., Robinson, M., Sujit, S. J., Datta, S., Sun, X., ... & Gabr, R. E. (2018). Multimodal MRI segmentation of brain tissue and T2-hyperintense white matter lesions in multiple sclerosis using deep convolutional neural networks and a large multi-center image database. 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), 1, 13–16.
Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A., Iqbal, M., & Saba, T. (2022). Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering. Microscopy Research and Technique, 85(1), 339–351.
Nayak, M. M., & Kengeri Anjanappa, S. D. (2023). An efficient hybrid classifier for MRI brain images classification using machine learning based naive Bayes algorithm. SN Computer Science, 4(3), Article 223.
Nida, N., Irtaza, A., Javed, A., Yousaf, M. H., & Mahmood, T. (2019). Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics, 124, 37–48.
Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., & Shen, D. (2018). 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Transactions on Cybernetics, 49(3), 1123–1136.
Nigudgi, S., & Bhyri, C. (2023). Lung cancer CT image classification using hybrid-SVM transfer learning approach. Soft Computing, 27(14), 9845–9859.
Nija, K. S., Anupama, C. P., Gopi, V. P., & Anitha, V. S. (2020). Automated segmentation of optic disc using statistical region merging and morphological operations. Physical and Engineering Sciences in Medicine, 43(3), 857–869.
Nitish, Singh, A. K., & Singla, R. (2020). Different approaches of classification of brain tumor in MRI using Gabor filters for feature extraction. Advances in Intelligent Systems and Computing, 1053, 1175–1188.
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., ... & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
Otsu, N. (1996). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
Öztürk, Ş. (2020). Stacked auto-encoder based tagging with deep features for content-based medical image retrieval. Expert Systems with Applications, 161, Article 113693.
Pandey, S., Singh, P. R., & Tian, J. (2020). An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation. Biomedical Signal Processing and Control, 57, Article 101782.
Panse, V., & Gupta, R. (2021). Medical Image Enhancement with Brightness Preserving Based on Local Contrast Stretching and Global Dynamic Histogram Equalization. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 164–170). IEEE.
Park, J., Park, S., Cho, W., Kim, S., Kim, G., Ahn, G., ... & Lim, J. (2011). Segmentation and visualization of anatomical structures from volumetric medical images. Image Processing: Machine Vision Applications IV, 7877, Article 78770U.
Pashaei, E., & Pashaei, E. (2023). Gaussian quantum arithmetic optimization-based histogram equalization for medical image enhancement. Multimedia Tools and Applications, 82(10), 1–21.
Pathan, S., & Tripathi, A. (2020). Y-net: Biomedical image segmentation and clustering. arXiv preprint arXiv:2004.05698.
Paul, A., Mukherjee, D. P., Das, P., Gangopadhyay, A., Chintha, A. R., & Kundu, S. (2018). Improved random forest for classification. IEEE Transactions on Image Processing, 27(8), 4012–4024.
Pitchai, R., Supraja, P., Sulthana, R. A., Veeramakali, T., & Babu, C. M. (2023). MRI image analysis for cerebrum tumor detection and feature extraction using 2D U-ConvNet and SVM classification. Personal and Ubiquitous Computing, 27(3), 931–940.
Pitchai, R., Supraja, P., Victoria, A. H., & Madhavi, M. (2021). Brain tumor segmentation using deep learning and fuzzy K-means clustering for magnetic resonance images. Neural Processing Letters, 53(4), 2519–2532.
Prakash, K., & Saradha, S. (2021). Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images. Materials Today: Proceedings, 45, 2–7.
Qiao, N., Sun, C., Sun, J., & Song, C. (2021). Superpixel combining region merging for pancreas segmentation. In 2021 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 276–281). IEEE.
Rączkowski, Ł., Możejko, M., Zambonelli, J., & Szczurek, E. (2019). ARA: Accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Scientific Reports, 9(1), 1–12.
Rahman, A., Muniyandi, R., & Albashish, D. (2021). Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer. PeerJ Computer Science, 7, 1–27.
Raja, N. S. M., Fernandes, L. S., Dey, N., Satapathy, S. C., & Rajinikanth, V. (2018). Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing, 9, 1–12.
Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., ... & Ng, A. Y. (2017). MURA: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:1712.06957.
Ramasamy, U. K., & P., A. (2019). SVM classification of brain images from MRI scans using morphological transformation and GLCM texture features. International Journal of Computational Systems Engineering, 5(1), Article 1.
Rao, C. S., & Karunakara, K. (2022). Efficient detection and classification of brain tumor using kernel based SVM for MRI. Multimedia Tools and Applications, 81(5), 7393–7417.
Rashighi, M., & Harris, J. E. (2017). Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. Physiology & Behavior, 176(3), 139–148.
Rashmi, R., Prasad, K., & Udupa, C. B. K. (2022). Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review. Journal of Medical Systems, 46(1), Article 4.
Reddy, A. S., & Chenna Reddy, P. (2018). Novel algorithm based on region growing method for better image segmentation. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 229–234). IEEE.
Rehman, M. U., Cho, S., Kim, J. H., & Chong, K. T. (2020). BU-Net: Brain tumor segmentation using modified U-Net architecture. Electronics, 9(12), 1–12.
Ren, J., Karagoz, K., Gatza, M. L., Singer, E. A., Sadimin, E., Foran, D. J., & Qi, X. (2018). Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks. Journal of Medical Imaging, 5(4), Article 047501.
Renita, D. B., & Christopher, C. S. (2020). Real time content based medical image retrieval scheme with GWO-SVM. Multimedia Tools and Applications, 79(23-24), 17227–17243.
Renuka Devi, K., Suganyadevi, S., & Balasamy, K. (2022). Healthcare data analysis using deep learning paradigm. In Deep Learning for Cognitive Computing Systems (pp. 129–148). De Gruyter.
Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., ... & Choi, G. S. (2021). An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity, 2021, 1–12.
Rezaei, M., Yang, H., & Meinel, C. (2020). Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimedia Tools and Applications, 79(21-22), 15329–15348.
Ristovski, G., Garbers, N., Hahn, H. K., Preusser, T., & Linsen, L. (2019). Uncertainty-aware visual analysis of radiofrequency ablation simulations. Computers & Graphics, 79, 24–35.
Rocha, M. M. M., Landini, G., & Florindo, J. B. (2023). Medical image classification using a combination of features from convolutional neural networks. Multimedia Tools and Applications, 82(13), 19299–19322.
Rodrigues, C., Peixoto, Z. M. A., & Ferreira, F. M. F. (2019). Ultrasound image denoising using wavelet thresholding methods in association with the bilateral filter. IEEE Latin America Transactions, 17(11), 1800–1807.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer.
Roth, H. R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., ... & Mori, K. (2018). An application of cascaded 3D fully convolutional networks for medical image segmentation. Computerized Medical Imaging and Graphics, 66, 90–99.
Ruikar, D. D., Santosh, K. C., & Hegadi, R. S. (2019). Contrast stretching-based unwanted artifacts removal from CT images. In Information and Communication Technology for Competitive Strategies (pp. 3–14). Springer.
Rundo, L., Tangherloni, A., Nobile, M. S., Militello, C., Besozzi, D., Mauri, G., & Cazzaniga, P. (2019). MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Systems with Applications, 119, 387–399.
S, S., V, S., P, A., & K, R. (2023). Integrated model for Covid 19 disease diagnosis using deep learning approach. In 2023 2nd International Conference on Edge Computing and Applications (ICECAA) (pp. 576–582). IEEE.
Sagar, P., Upadhyaya, A., Mishra, S. K., Pandey, N. R., Sahu, S. S., & Panda, G. (2020). A circular adaptive median filter for salt and pepper noise suppression from MRI images. Journal of Scientific and Industrial Research, 79(10), 941–944.
Saifullah, S., Drezewski, R., Khaliduzzaman, A., Tolentino, L. K., & Ilyos, R. (2022). K-means segmentation based-on lab color space for embryo detection in incubated egg. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 8(2), 175–184.
Saikia, T., Hansdah, M., Singh, K. K., & Bajpai, M. K. (2022). Classification of lung nodules based on transfer learning with K-nearest neighbor (KNN). In 2022 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 1–6). IEEE.
Salih, O., & Viriri, S. (2020). Skin lesion segmentation using stochastic region-merging and pixel-based Markov random field. Symmetry, 12(8), Article 1224.
Santos, L., Veras, R., Aires, K., Britto, L., & Machado, V. (2018). Medical image segmentation using seeded fuzzy C-means: A semi-supervised clustering algorithm. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE.
Saood, A., & Hatem, I. (2021). COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21(1), 1–10.
Sari, C. T., & Gunduz-Demir, C. (2019). Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images. IEEE Transactions on Medical Imaging, 38(5), 1139–1149.
Sarker, M. M. K., Rashwan, H. A., Akram, F., Singh, V. K., Banu, S. F., Chowdhury, F. U. H., ... & Puig, D. (2021). SLSNet: Skin lesion segmentation using a lightweight generative adversarial network. Expert Systems with Applications, 183, Article 115433.
Saturi, R., & Parvataneni, P. C. (2022). Histopathology breast cancer detection and classification using optimized superpixel clustering algorithm and support vector machine. Journal of The Institution of Engineers (India): Series B, 103(5), 1589–1603.
Sejuti, Z. A., & Islam, M. S. (2023). A hybrid CNN–KNN approach for identification of COVID-19 with 5-fold cross validation. Sensors International, 4, Article 100229.
Senan, E. M., & Jadhav, M. E. (2021). Techniques for the detection of skin lesions in PH2 dermoscopy images using local binary pattern (LBP). In Advanced Computing (pp. 129–140). Springer.
Senthilkumaran, N., & Vaithegi, S. (2016). Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering: An International Journal, 6(1), 1–13.
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2020). A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, 205密, Article 106270.
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2021). Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy. Pattern Recognition, 119, Article 108110.
Shaheed, K., Szczuko, P., Abbas, Q., Hussain, A., & Albathan, M. (2023). Computer-aided diagnosis of COVID-19 from chest X-ray images using hybrid-features and random forest classifier. Healthcare, 11(6), Article 837.
Sharif, M. I., Li, J. P., Naz, J., & Rashid, I. (2020). A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters, 131, 30–37.
Sharma, A., Kumar, S., & Singh, N. N. (2019). Brain tumor segmentation using DE embedded OTSU method and neural network. Multidimensional Systems and Signal Processing, 30(3), 1263–1291.
Sharma, H., Zerbe, N., Lohmann, S., Kayser, K., Hellwich, O., & Hufnagl, P. (2015). A review of graph-based methods for image analysis in digital histopathology. Diagnostic Pathology, 1(1), 1–51.
Shaukat, F., Raja, G., Ashraf, R., Khalid, S., Ahmad, M., & Ali, A. (2019). Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. Journal of Ambient Intelligence and Humanized Computing, 10(10), 4135–4149.
Shi, J., Wang, R., Zheng, Y., Jiang, Z., Zhang, H., & Yu, L. (2021). Cervical cell classification with graph convolutional network. Computer Methods and Programs in Biomedicine, 198, Article 105807.
Shia, W. C., Hsu, F. R., Dai, S. T., Guo, S. L., & Chen, D. R. (2022). Semantic segmentation of the malignant breast imaging reporting and data system lexicon on breast ultrasound images by using DeepLab v3. Sensors, 22(14), Article 5352.
Shirazi, A. Z., Fornaciari, E., McDonnell, M. D., Yaghoobi, M., Cevallos, Y., Tello-Oquendo, L., ... & Gomez, G. A. (2020). The application of deep convolutional neural networks to brain cancer images: A survey. Journal of Personalized Medicine, 10(4), 1–27.
Sivakumar, V., & Janakiraman, N. (2020). A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA. BioSystems, 198, Article 104226.
Smith, A. R. (1978). Color gamut transform pairs. Computer Graphics (ACM), 12(3), 12–19.
Sonali, Sahu, S., Singh, A. K., Ghrera, S. P., & Elhoseny, M. (2019). An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics & Laser Technology, 110, 87–98.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462.
Srinivas, B., & Sasibhushana Rao, G. (2019). A hybrid CNN-KNN model for MRI brain tumor classification. International Journal of Recent Technology and Engineering, 8(2), 5230–5235.
Subramani, B., & Veluchamy, M. (2020). Fuzzy gray level difference histogram equalization for medical image enhancement. Journal of Medical Systemsヌ, 44(6), Article 105.
Subudhi, A., Dash, M., & Sabut, S. (2020). Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics and Biomedical Engineering, 40(1), 277–289.
Sucharitha, G., & Senapati, R. K. (2019). Local extreme co-occurrence edge binary pattern for bio-medical image retrieval. In 2019 2nd International Conference on Advanced Computational and Communication Paradigms (ICACCP) (pp. 1–6). IEEE.
Suganyadevi, S., Renukadevi, K., Balasamy, K., & Jeevitha, P. (2022). Diabetic retinopathy detection using deep learning methods. In 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) (pp. 1–6). IEEE.
Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2022b). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19–38.
Sun, C., Li, B., Wei, G., Qiu, W., Li, D., Li, X., ... & Liang, L. (2022). Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer. Computer Methods and Programs in Biomedicine, 221, Article 106914.
Sun, J., Peng, Y., Guo, Y., & Li, D. (2021). Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing, 423, 34–45.
Sureka, M., Patil, A., Anand, D., & Sethi, A. (2020). Visualization for histopathology images using graph convolutional neural networks. In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 331–335). IEEE.
Swaroopa, H. N., Jagadale, B. N., Priya, B. S., Alnaggar, O. A. M. F., & Abhisheka, T. E. (2022). Bio-medical image segmentation using wavelet based fusion technique. Biomedical and Pharmacology Journal, 15(2), 767–773.
Tahir, B., Iqbal, S., Usman Ghani Khan, M., Saba, T., Mehmood, Z., Anjum, A., & Mahmood, T. (2019). Feature enhancement framework for brain tumor segmentation and classification. Microscopy Research and Technique, 82(6), 803–811.
Tamilmani, G., & Sivakumari, S. (2019). Early detection of brain cancer using association allotment hierarchical clustering. International Journal of Imaging Systems and Technology, 29(4), 617–632.
Tan, J., Jing, L., Huo, Y., Li, L., Akin, O., & Tian, Y. (2021). LGAN: Lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics, 87, Article 101817.
Tang, W., Zou, D., Yang, S., Shi, J., Dan, J., & Song, G. (2020). A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Computing and Applications, 32(11), 6769–6778.
Thayumanavan, M., & Ramasamy, A. (2021). An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier. Concurrent Engineering Research and Applications, 29(3), 266–274.
Tomar, N. K., Jha, D., Riegler, M. A., Johansen, H. D., Johansen, D., Rittscher, J., ... & Ali, S. (2022). FANet: A feedback attention network for improved biomedical image segmentation. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 9375–9388.
Tschandl, P., Rosendahl, C., & Kittler, H. (2018). Data descriptor: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, Article 180161.
Tumpa, P. P., & Kabir, A. (2021). An artificial neural network based detection and classification of melanoma skin cancer using hybrid texture features. Sensors International, 2, Article 100128.
Ullah, S., Khalid, S., Hussain, F., Hassan, A., & Riaz, F. (2019). Curve evolution based on edge following algorithm for medical image segmentation. In Advances in Intelligent Systems and Computing (Vol. 868, pp. 529–538). Springer.
Van Nguyen, S., Tran, H. M., & Le, T. S. (2020). Application of geometric modeling in visualizing the medical image dataset. SN Computer Science, 1(5), Article 254.
Vania, M., & Lee, D. (2021). Intervertebral disc instance segmentation using a multistage optimization mask-RCNN (MOM-RCNN). Journal of Computational Design and Engineering, 8(4), 1023–1036.
Venkatachalam, K., Siuly, S., Bacanin, N., Hubalovsky, S., & Trojovsky, P. (2021). An efficient Gabor Walsh-Hadamard transform based approach for retrieving brain tumor images from MRI. IEEE Access, 9, 119078–119089.
Vijh, S., Saraswat, M., & Kumar, S. (2023). Automatic multilevel image thresholding segmentation using hybrid bio-inspired algorithm and artificial neural network for histopathology images. Multimedia Tools and Applications, 82(4), 4979–5010.
Vijila Rani, K., & Joseph Jawhar, S. (2022). Lung lesion classification scheme using optimization techniques and hybrid (KNN-SVM) classifier. IETE Journal of Research, 68(2), 1485–1499.
Vikhe, P. S., Mandhare, V. V., & Kadu, C. B. (2022). Mass detection in mammographic images using improved marker-controlled watershed approach. International Journal of Biomedical Engineering and Technology, 40(1), 70–82.
Vivona, L., Cascio, D., Taormina, V., & Raso, G. (2018). Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method. IET Computer Vision, 12(7), 989–995.
Vogado, L. H. S., Veras, R. M. S., Araujo, F. H. D., Silva, R. R. V., & Aires, K. R. T. (2018). Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence, 72, 415–422.
Vupputuri, A., Ashwal, S., Tsao, B., & Ghosh, N. (2020). Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering. Computers in Biology and Medicine, 116, Article 103536.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(3), 328–339.
Wang, B., Lei, Y., Jeong, J. J., Wang, T., Liu, Y., Tian, S., & Patel, P. (2019a). Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution. Medical Imaging 2019: Computer-Aided Diagnosis, 10950, Article 109503X.
Wang, B., Lei, Y., Tian, S., Wang, T., Liu, Y., Patel, P., ... & Yang, X. (2019b). Deeply supervised 3D FCN with group dilated convolution for automatic MRI prostate segmentation. Medical Physics, 46(4), 1707–1718.
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019c). Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing, 338, 34–45.
Wang, J., Chen, R. J., Lu, M. Y., Baras, A., & Mahmood, F. (2020). Weakly supervised prostate TMA classification via graph convolutional networks. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 239–243). IEEE.
Wang, J., & Liu, X. (2021). Medical image recognition and segmentation of pathological slices of gastric cancer based on DeepLab v3+ neural network. Computer Methods and Programs in Biomedicine, 207, Article 106210.
Wang, K. S., Yu, G., Xu, C., Meng, X. H., Zhou, J., Zheng, C., ... & Deng, H. W. (2021). Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Medicine, 19(1), 1–12.
Wang, R., Chen, S., Ji, C., Fan, J., & Li, Y. (2022a). Boundary-aware context neural network for medical image segmentation. Medical Image Analysis, 78, Article 102395.
Wang, S., Chen, Z., You, S., Wang, B., Shen, Y., & Lei, B. (2022b). Brain stroke lesion segmentation using consistent perception generative adversarial network. Neural Computing and Applications, 34(11), 8657–8669.
Wang, Z., Zou, Y., & Liu, P. X. (2021b). Hybrid dilation and attention residual U-Net for medical image segmentation. Computers in Biology and Medicine, 134, Article 104449.
Weiss, J., & Navab, N. (2021). Deep direct volume rendering: Learning visual feature mappings from exemplary images. arXiv preprint arXiv:2106.05429.
Weiss, S., & Westermann, R. (2022). Differentiable direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 28(1), 562–572.
Wen, Y., Zhang, L., Meng, X., & Ye, X. (2023). Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy. IEEE Access, 11, 16183–16193.
Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., & Yu, S. (2021). A survey on incorporating domain knowledge into deep learning for medical image analysis. Medical Image Analysis, 69密, Article 101985.
Xing, W., & Bei, Y. (2019). Medical health big data classification based on KNN classification algorithm. IEEE Access, 8, 28808–28819.
Xiong, Y., Ye, M., & Wu, C. (2021). Cancer classification with a cost-sensitive naive Bayes stacking ensemble. Computational and Mathematical Methods in Medicine, 2021, Article 5556992.
Xu, H., Lu, C., Berendt, R., Jha, N., & Mandal, M. (2018). Automated analysis and classification of melanocytic tumor on skin whole slide images. Computerized Medical Imaging and Graphics, 66, 124–134.
Xu, H., Park, S., & Hwang, T. H. (2020). Computerized classification of prostate cancer Gleason scores from whole slide images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1871–1882.
Xu, J., Thevenon, G., Chabat, T., McCormick, M., Li, F., Birdsong, T., ... & Aylward, S. (2023a). Interactive, in-browser cinematic volume rendering of medical images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4), 1019–1026.
Xu, Q., Ma, Z., He, N., & Duan, W. (2023b). DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation. Computers in Biology and Medicine, 154, Article 106626.
Xu, Y., Jia, Z., Wang, L.-B., Ai, Y., Zhang, F., Lai, M., & Chang, E. I. C. (2017). Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics, 18(1), Article 281.
Xue, Y., Xu, T., Zhang, H., Long, L. L., & Huang, X. (2018). SegAN: Adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics, 16(3-4), 383–392.
Yadav, S. S., & Jadhav, M. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6(1), Article 57.
Yan, L., & Zhang, J. (2019). Image segmentation of rice blast disease based on two-dimensional histogram in HSI space. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 1160–1165). IEEE.
Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., ... & Zhang, F. (2020). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173, 52–60.
Yang, H., Nan, G., Lin, M., Chao, F., Shen, Y., Li, K., & Ji, R. (2022). LAB-Net: LAB color-space oriented lightweight network for shadow removal. arXiv preprint arXiv:2208.13039.
Yang, J., Tu, J., Zhang, X., Yu, S., & Zheng, X. (2023). TSE DeepLab: An efficient visual transformer for medical image segmentation. Biomedical Signal Processing and Control, 80, Article 104376.
Yang, Y., Hu, Y., Zhang, X., & Wang, S. (2022b). Two-stage selective ensemble of CNN via deep tree training for medical image classification. IEEE Transactions on Cybernetics, 52(9), 9194–9207.
Yang, Y., Wang, R., & Feng, C. (2020). Level set formulation for automatic medical image segmentation based on fuzzy clustering. Signal Processing, 87, Article 115907.
Yang, Y., Zhang, W., Liang, D., & Yu, N. (2018). A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images. Multimedia Tools and Applications, 77(14), 18043–18065.
Yao, H., Zhang, X., Zhou, X., & Liu, S. (2019). Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers, 11(12), 1–14.
Yao, Y., Chen, Y., Gou, S., Chen, S., Zhang, X., & Tong, N. (2023). Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network. Biomedical Signal Processing and Control, 83, Article 104583.
Ye, H., Wang, D.-H., Li, J., Zhu, S., & Zhu, C. (2019). Improving histopathological image segmentation and classification using graph convolution network. In Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition (pp. 192–198).
Yu, J.-G., Wu, Z., Ming, Y., Deng, S., Wu, Q., Xiong, Z., ... & Li, Y. (2023). Bayesian collaborative learning for whole-slide image classification. IEEE Transactions on Medical Imaging, 42(6), 1809–1821.
Zaw, H. T., Maneerat, N., & Win, K. Y. (2019). Brain tumor detection based on Naïve Bayes classification. In Proceeding - 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST) (pp. 1–4). IEEE.
Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Zebari, D. A. (2019). Machine learning and region growing for breast cancer segmentation. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 88–93). IEEE.
Zhang, C., Schultz, T., Lawonn, K., Eisemann, E., & Vilanova, A. (2016). Glyph-based comparative visualization for diffusion tensor fields. IEEE Transactions on Visualization and Computer Graphics, 22(1), 797–806.
Zhang, J., Hua, Z., Yan, K., Tian, K., Yao, J., Liu, E., ... & Han, X. (2021a). Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images. Medical Image Analysis, 73, Article 102183.
Zhang, J., Li, C., Kosov, S., Grzegorzek, M., Shirahama, K., Jiang, T., ... & Li, H. (2021b). LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recognition, 115, Article 107885.
Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J., ... & Grzegorzek, M. (2022a). A comprehensive review of image analysis methods for microorganism counting: From classical image processing to deep learning approaches. Artificial Intelligence Review, 55(4), 2819–2838.
Zhang, K., Shi, Y., Hu, C., & Yu, H. (2022b). Nucleus image segmentation method based on GAN and FCN model. Soft Computing, 26(16), 7449–7460.
Zhao, T., Hoffman, J., McNitt-Gray, M., & Ruan, D. (2019). Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics. Medical Physics, 46(1), 190–198.
Zheng, Q., Li, H., Fan, B., Wu, S., & Xu, J. (2018). Integrating support vector machine and graph cuts for medical image segmentation. Journal of Visual Communication and Image Representation, 55, 157–165.
Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.-A., & Rajpoot, N. (2019). Cell graph convolutional network for grading of colorectal cancer histology images. In IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (pp. 388–398). IEEE.
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis (pp. 3–11). Springer.
Zhuang, J., Cai, J., Wang, R., Zhang, J., & Zheng, W. S. (2020). Deep KNN for medical image classification. In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging (pp. 127–136). Springer.
