Breast Lesion Detection from Mammograms Using Deep Convolutional Neural Networks

Mammography has a central role in screening and diagnosis of breast lesions, allowing early detection of the pathology and reduction of fatal cases. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. In the present paper, we illustrate the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks not yet used in literature. For this reason, a traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. Preliminary numerical results are detailed and future plans outlined.