Breast cancer detection in digital mammography using phase features and machine learning approach

Julia Diaz-Escobar; Vitaly Kober; Arnoldo Díaz-Ramírez

doi:10.1117/12.2632184

3 October 2022 Breast cancer detection in digital mammography using phase features and machine learning approach

Julia Diaz-Escobar, Vitaly Kober, Arnoldo Díaz-Ramírez

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270S (2022) https://doi.org/10.1117/12.2632184
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Conference Poster

Abstract

For several years, Computer-Aided Detection (CAD) systems have been used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by a CAD system, more false predictions must be revised by the expert to avoid an unnecessary biopsy. On the other hand, the research community has been exploring different approaches for the detection and classification of breast abnormalities. Machine learning, and particularly deep learning approaches, are being used to analyze digital mammography images. Nevertheless, most of the models proposed so far are trained on a single database and do not have high reliability. In this work, several deep learning models were compared for benign-malign mammography classification. A pre-processing stage is designed to remove noise and extract features using local image phase information. Then, a machine learning approach is utilized for digital mammography classification. Experimental results are presented using various digital mammography datasets and evaluated under different performance metrics.

Citation Download Citation

Julia Diaz-Escobar, Vitaly Kober, and Arnoldo Díaz-Ramírez "Breast cancer detection in digital mammography using phase features and machine learning approach", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270S (3 October 2022); https://doi.org/10.1117/12.2632184

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