Abstract
Breast cancer is the most commonly diagnosed cancer in women both before and after menopause. One in nine women will be diagnosed with breast cancer during their lifetime, and one in 27 women will die from it. This makes the diagnosis of this cancer a public health priority. Over the past 20 years, several diagnostic techniques have been proposed, including the method based on breast mammography, which is widely used today and allows the specialist doctor to make decisions based on the patient's scan images. To improve diagnostic outcomes, several artificial intelligence methods are used today, notably Convolutional Neural Networks, which have been particularly interesting in recent studies. In this work, we conducted a deep analysis on breast images from the “mias” database to classify people with malignant or benign cancer with great precision. We used pre-trained architectures (XCEPTION, VGG, RESNET, and INCEPTION) to experiment with transfer learning and achieved performance of up to 99.18% accuracy, 99% recall, and 100% precision.
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References
Afyf, A., Bellarbi, L., Achour, A., Riouch, F., Errachid, A.: A novel low cost UWB antenna for early breast cancer detection. Am. J. Electromagnet. Appl. 3(5), 31–37 (2015). https://doi.org/10.11648/j.ajea.20150305.11
Khrouch, S., Tsouli Fathi, M., El Yessefi, A., Cherrat, L., Rhalem, W., Ezziyyani, M.: Breast cancer progression prediction for care treatment efficiency and intelligent adaptation, based on AI algorithm classification. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 1–7. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_1
Hossinq, M.M., Molla, S., Shamrat, F.J.M., Samia, M.M., Saha, R.M. and Khater, A.: Comparative study on breast cancer classification using multiple convolution neural network architectures IEEE, 22 (11), (2022). https://doi.org/10.1109/ICOSEC54921.2022.9951881
Hajiarbabi, M.: Breast cancer using deep learning and histopathology images research square. 28(03) (2023) https://doi.org/10.21203/rs.3.rs-2738738/v1
Shen, L., et al.: Deep learning to improve breast cancer detection on screening mammography. Sci. Reports 29(08) (2019) https://doi.org/10.1038/s41598-019-48995-4
Khan, S., et al.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Patt. Recogn. Lett. 01(07), 1–6 (2019). https://doi.org/10.1016/j.patrec.2019.03.022
Golatkar, A., et al.: Classification of breast cancer histology using deep learning Lecture Notes in Computer Sciences 06(06) (2018). https://doi.org/10.1007/978-3-319-93000-8_95
Sharma, S., Mehra, R.: Conventional Machine Learning and Deep Learning Approach for Multi Classification of Breast Cancer Histopathology Images— a Comparative Insight Journal of Digital Imaging. 03 (01) (2020). https://doi.org/10.1007/s10278-019-00307-y
Nawaz, M., et al.: Multi-class breast cancer classification using deep learning convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 9(6), 316–332 (2018). f55bc7f5d3d90bd5cb239c0202b3428b6aa2-libre.pdf (d1wqtxts1xzle7.cloudfront.net),.
CNRST of Morocco.: The high-performance computing platform (HPC) (2022). https://hpc.marwan.ma/ Accessed 11 May 2022
Torrey, L., Shavlik, J.: Transfer Learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. In: Emilio Soria Olivas IGI Global, pp. 242–264 (2010)
You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., Keutzer, K.: ImageNet training in minutes. In: Proceedings of the 47th International Conference on Parallel Processing. Association for Computing Machinery, New York, NY, USA (2018)
Alzubaidi, L., et al.: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 53 (2021). https://doi.org/10.1186/s40537-021-0044
Retal, S., Sahbani, H., Kharmoum, N., Rhalem, W., Ezziyyani, M.: Machine learning for diabetes prediction. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 75–83. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_7
Gsim, J., et al.: Artificial intelligence for stroke prediction. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 359–367. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_32
Rhalem, W., et al.: Novel alignment approach of DNA sequences. In: El Moussati, A., Kpalma, K., Ghaouth Belkasmi, M., Saber, M., Guégan, S. (eds.) Advances in Smart Technologies Applications and Case Studies. SmartICT 2019. LNEE, vol. 684, pp. 489–497. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53187-4_53
Sedrati, H., et al.: Analysis of alcoholic EEG signals based on discrete to continuous algorithm. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 587–598. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_53
Aqili, N., et al.: New approach of 3D protein structure superimposition: case study of “SARS-COV-2” and “SARS-COV”. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 805–815. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_71
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This work was supported by the “Urgence COVID-19” fundraising campaign of the Institut Pasteur.
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Rhalem, W. et al. (2024). Deep Medical Imaging, Analysis the Brest Cancer Mammography. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_14
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