Abstract
Breast cancer (BC) is one of the malignant diseases that can affect women in the first degree. Its early diagnosis is necessary and effective in its early detection, which will contributes to its complete elimination, or slow down and delay its progression, and increases the success of treatment. Medical imaging plays an important role in the early diagnosis of diseases. In the case of BC, mammographic imaging allows clear visualization of the breast and discovery of BC in its early stages. Its classification is a technique that effectively helps radiologists in the analysis of medical images, it allows to detect pathologies and categorize them according to its appropriate stage. Deep convolutional neural network (DCNN) models have proven to be widely used in the medical field in recent years and achieve great feats in image classification, especially in terms of high performance and robustness. In this paper, we propose two approaches based on the Transfer Learning (TF) technique to classify BC using mammography images from the Mini-MIAS dataset. The first one consists in performing the classification process by fine-tuning the adopted pre-trained model with hyperparameters adjustment, while the second approach consists in using the pre-trained model as feature Extractor, then the classification phase is performed by Random Forest machine learning classifier. The DCNN model adopted in this work is VGG-19. Data augmentation technique is a necessary preprocessing step for the improvement of our small dataset. The experimental results show that the first approach classification give the highest accuracy of 97%.
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Laaffat, N., Outfarouin, A., Bouarifi, W., Jraifi, A. (2023). A Deep Convolutional Neural Networks for the Detection of Breast Cancer Using Mammography Images. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-031-26254-8_5
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