Abstract
The computer-aided diagnosis for breast cancer is coming more and more sought due to the exponential increase of performing mammograms. Particularly, diagnosis and classification of the mammary masses are of significant importance today. For this reason, numerous studies have been carried out in this field and many techniques have been suggested. This paper proposes a convolutional neural network (CNN) approach for automatic detection of breast cancer using the segmented data from digital database for screening mammography (DDSM). We develop a network with CNN architecture that avoids the extracting traditional handcrafted feature phase by processing the extraction of features and classification at one time within the same network of neurons. Therefore, it provides an automatic diagnosis without the user admission. The proposed method offers better classification rates, which allows a more secure diagnosis of breast cancer.
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Benzebouchi, N.E., Azizi, N., Ayadi, K. (2019). A Computer-Aided Diagnosis System for Breast Cancer Using Deep Convolutional Neural Networks. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_52
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DOI: https://doi.org/10.1007/978-981-10-8055-5_52
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