Convolutional neural networks for automatic detection of breast pathologies

Jose A. Gonzalez-Fraga; Vitaly Kober; Everardo Gutierrez-Lopez; J. Alejandro Gonzalez-Sarabia

doi:10.1117/12.2633449

3 October 2022 Convolutional neural networks for automatic detection of breast pathologies

Jose A. Gonzalez-Fraga, Vitaly Kober, Everardo Gutierrez-Lopez, J. Alejandro Gonzalez-Sarabia

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122260Z (2022) https://doi.org/10.1117/12.2633449
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Breast cancer in women is a worldwide health problem that has a high mortality rate. A strategy to reduce breast cancer mortality in women is to implement preventive programs such as mammography screening for early breast cancer diagnosis. In this presentation, a method for automatic detection of breast pathologies using a deep convolutional neural network and a class activation map is proposed. The neural network is pretrained on the regions of interest in order to modify the output layers to have two output classes. The proposed method is compared with different CNN models and applied to classify the public dataset Curated Breast Imaging Subset of DDSM (CBIS-DDSM).

Conference Presentation

Citation Download Citation

Jose A. Gonzalez-Fraga, Vitaly Kober, Everardo Gutierrez-Lopez, and J. Alejandro Gonzalez-Sarabia "Convolutional neural networks for automatic detection of breast pathologies", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122260Z (3 October 2022); https://doi.org/10.1117/12.2633449

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