Localization, segmentation, and classification of mammographic abnormalities using deep learning

Adeela Islam; Zobia Suhail; Reyer Zwiggelaar

doi:10.1117/12.3026998

29 May 2024 Localization, segmentation, and classification of mammographic abnormalities using deep learning

Adeela Islam, Zobia Suhail, Reyer Zwiggelaar

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741Q (2024) https://doi.org/10.1117/12.3026998
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Breast cancer is a disease caused by abnormal growth of cells in the breast. We have investigated a deep learning pipeline, which provides classification (e.g. normal/ abnormal), and subsequently localization and segmentation of abnormalities. We have used the digital database for screening mammography in this work. The contributions of this paper are two-fold. First, we classify between normal and abnormal mammograms with a 100% training and 98.34% testing accuracy. Second, a framework is proposed to localize and segment abnormalities from abnormal images with a training loss of 0.57 and a testing loss of 0.55 where the multi-task loss function combines the loss of classification, localization, and segmentation mask.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Adeela Islam, Zobia Suhail, and Reyer Zwiggelaar "Localization, segmentation, and classification of mammographic abnormalities using deep learning", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741Q (29 May 2024); https://doi.org/10.1117/12.3026998

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