Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment
DOI:
https://doi.org/10.1016/j.compbiolchem.2024.108110Keywords:
Breast cancer , Deep learning , Feature selection , Growth Optimizer , Internet of Medical ThingsAbstract
The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.
References
Abualigah, L., et al. (2022). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158. https://doi.org/10.1016/j.eswa.2021.116158
Abualigah, L., et al. (2022). Efficient text document clustering approach using multi-search arithmetic optimization algorithm. Knowledge-Based Systems, 238, 107847. https://doi.org/10.1016/j.knosys.2021.107847
Atban, F., et al. (2023). Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomedical Signal Processing and Control, 81, 104443. https://doi.org/10.1016/j.bspc.2022.104443
Buciu, I., & Gacsadi, A. (2011). Directional features for automatic tumor classification of mammogram images. Biomedical Signal Processing and Control, 6(4), 370-378. https://doi.org/10.1016/j.bspc.2011.02.001
Chaudhury, S., et al. (2023). A blockchain-enabled internet of medical things system for breast cancer detection in healthcare. Healthcare Analytics, 3, 100142. https://doi.org/10.1016/j.health.2023.100142
Gonçalves, C. B., et al. (2022). CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Computers in Biology and Medicine, 142, 105205. https://doi.org/10.1016/j.compbiomed.2021.105205
Houssein, E. H., et al. (2021). An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Systems with Applications, 185, 115651. https://doi.org/10.1016/j.eswa.2021.115651
Huang, M. L., et al. (2020). Dataset of breast mammography images with masses. Data in Brief, 31, 105834. https://doi.org/10.1016/j.dib.2020.105834
Liu, L., et al. (2021). Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Computers in Biology and Medicine, 138, 104910. https://doi.org/10.1016/j.compbiomed.2021.104910
Moreira, I. C., et al. (2012). INbreast: toward a full-field digital mammographic database. Academic Radiology, 19(2), 236-248. https://doi.org/10.1016/j.acra.2011.09.014
Muduli, D., et al. (2022). Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach. Biomedical Signal Processing and Control, 71, 102825. https://doi.org/10.1016/j.bspc.2021.102825
Nassif, A. B., et al. (2022). Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artificial Intelligence in Medicine, 127, 102276. https://doi.org/10.1016/j.artmed.2022.102276
Rampun, A., et al. (2020). Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns. Computers in Biology and Medicine, 121, 103774. https://doi.org/10.1016/j.compbiomed.2020.103774
Sadad, T., et al. (2018). Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature. Journal of Computational Science, 29, 34-45. https://doi.org/10.1016/j.jocs.2018.09.015
Singh, L. K., et al. (2024). Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images. Medical Engineering & Physics, 124, 104117. https://doi.org/10.1016/j.medengphy.2023.104117
Singh, L. K., et al. (2023). Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Advanced Engineering Informatics, 58, 102146. https://doi.org/10.1016/j.aei.2023.102146
Thawkar, S., et al. (2021). Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Computers in Biology and Medicine, 139, 104968. https://doi.org/10.1016/j.compbiomed.2021.104968
Zhang, Q., et al. (2023). Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems. Knowledge-Based Systems, 261, 110206. https://doi.org/10.1016/j.knosys.2022.110206
Abbas, S., et al. (2021). BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm. PeerJ Computer Science, 7, e390. https://doi.org/10.7717/peerj-cs.390
Ahmad, F., et al. (2015). A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. Pattern Analysis and Applications, 18(4), 861-870. https://doi.org/10.1007/s10044-014-0402-9
Aldhyani, T. H. H., et al. (2023). A secure internet of medical things framework for breast cancer detection in sustainable smart cities. Electronics, 12(10), 2261. https://doi.org/10.3390/electronics12102261
Almodfer, R., et al. (2022). Improving parameter estimation of fuel cell using honey badger optimization algorithm. Frontiers in Energy Research, 10, 851082. https://doi.org/10.3389/fenrg.2022.851082
Chakravarthy, S. R. S., et al. (2023). Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer. IRBM, 44(4), 100771. https://doi.org/10.1016/j.irbm.2023.100771
Ewees, A. A., et al. (2021). Improved slime mould algorithm based on firefly algorithm for feature selection: A case study on QSAR model. Engineering with Computers, 38(3), 2407-2421. https://doi.org/10.1007/s00366-021-01342-6
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