Breast Cancer classification via Deep Learning approaches
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Published
Apr 10, 2024
Marco Gagliardi
Tommaso Ruga
Ester Zumpano
Eugenio Vocaturo
https://orcid.org/0000-0001-7457-7118
Abstract
Breast cancer is the most common type of cancer in women worldwide. In 2023, there were 2.296.840 (23,8% of all women with cancer) new diagnoses. Early diagnosis is a key factor in reducing the mortality rate of breast cancer. One of the screening methods used to prevent breast cancer is breast ultrasound. In this paper, a new model is proposed that starts from a resnet101 and increases the classification capacity of a normal resnet101. Experimental studies show how deep learning models can successfully classify breast ultrasound images. The proposed model achieves 91% accuracy with convergence in less than 30 epochs. This study shows that deep learning models are effective in classifying ultrasound images and could be used by a radiologist to increase the accuracy of diagnoses.
How to Cite
Gagliardi, M., Ruga, T., Zumpano, E., & Vocaturo, E. (2024). Breast Cancer classification via Deep Learning approaches. SPAST Reports, 1(4). Retrieved from https://spast.org/ojspath/article/view/4992
Article Details
Keywords
Deep learning; Breast cancer; Classification; Convolutional Neural Network
References
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4. Ting, F. F., Tan, Y. J., \& Sim, K. S. (2019). Convolutional neural network improvement for breast cancer classification. Expert Systems with Applications, 120, 103-115. https://doi.org/10.1016/j.eswa.2018.11.008
5. Al-Dhabyani, W., et al. (2020). Dataset of breast ultrasound images. Data in brief, 28, 104863.
https://doi.org/10.1016/j.dib.2019.104863
6. Rodrigues, P.S. (2017), “Breast Ultrasound Image”, Mendeley Data, V1, https://doi.org/10.17632/wmy84gzngw.1
7. Ragab, M. et al. (2022). Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images. Biology, 11(3), 439. https://doi.org/10.3390/biology11030439
8. Kalafi et al.(2021). Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics, 11(10). https://doi.org/10.3390/diagnostics11101859
9. PACAL, İ. (2022). Deep learning approaches for classification of breast cancer in ultrasound (US) images. Journal of the Institute of Science and Technology, 12(4), 1917-1927. https://doi.org/10.21597/jist.1183679
10. Xie et al.(2020). A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors. Physics in Medicine & Biology, 65(24), 245001.
https://doi.org/10.1088/1361-6560/abc5c7
11. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385. https://doi.org/10.48550/arXiv.1512.03385
12. Kaiser, L., Gomez, A. N.,& Chollet, F. (2017). Depthwise separable convolutions for neural machine translation. arXiv preprint arXiv:1706.03059. https://doi.org/10.48550/arXiv.1706.03059
13. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). DOI: 10.1109/CVPR.2017.243
14. Wang, J., Liu, Q., Xie, H., Yang, Z., \& Zhou, H. (2021). Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers, 13(4), 661. https://doi.org/10.3390/cancers13040661
15. Zhuang F. et al.(2020). A comprehensive survey on transfer learning. Proceedings IEEE, 109(1), 43-76. DOI: 10.1109/JPROC.2020.3004555
16. Luciano Caroprese and Eugenio Vocaturo and Ester Zumpano. Argumentation approaches for explanaible AI in medical informatics, Intelligent Systems with Applications, v(16), pg 200109, 2022, 2667-3053, https://doi.org/10.1016/j.iswa.2022.200109
17. Pradhan, N and Dhaka, V. S and Rani, G and Pradhan, V and Vocaturo, E and Zumpano, E. Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs Into 3 Dimensional Views, IEEE Access, 2023, v(11), pg={96283-96296} DOI: 10.1109/ACCESS.2023.3307198
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