A comparative study of convolutional network models for the classification of abnormalities in mammograms

J. Manuel Valencia-Moreno; J. Alejandro Gonzalez Sarabia; J. Angel González-Fraga; Everardo Gutiérrez-Lopez; Vitaly Kober

doi:10.1117/12.2633799

3 October 2022 A comparative study of convolutional network models for the classification of abnormalities in mammograms

J. Manuel Valencia-Moreno, J. Alejandro Gonzalez Sarabia, J. Angel González-Fraga, Everardo Gutiérrez-Lopez, Vitaly Kober

Author Affiliations +

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122261I (2022) https://doi.org/10.1117/12.2633799
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Conference Poster

Abstract

Breast cancer is the most common cancer and one of the main causes of death in women. Early diagnosis of breast cancer is essential to ensure a high chance of survival for the affected women. Computer-aided detection (CAD) systems based on convolutional neural networks (CNN) could assist in the classification of abnormalities such as masses and calcifications. In this paper, several convolutional network models for the automatic classification of pathology in mammograms are analyzed. As well as different preprocessing and tuning techniques, such as data augmentation, hyperparameter tuning, and fine-tuning are used to train the models. Finally, these models are validated on various publicly available benchmark datasets.

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J. Manuel Valencia-Moreno, J. Alejandro Gonzalez Sarabia, J. Angel González-Fraga, Everardo Gutiérrez-Lopez, and Vitaly Kober "A comparative study of convolutional network models for the classification of abnormalities in mammograms", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122261I (3 October 2022); https://doi.org/10.1117/12.2633799

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