Automatic classification and detection of abnormalities in mammograms using deep learning

Adeela Islam; Zobia Suhail

doi:10.1117/12.2624216

13 July 2022 Automatic classification and detection of abnormalities in mammograms using deep learning

Adeela Islam, Zobia Suhail

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860V (2022) https://doi.org/10.1117/12.2624216
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Breast cancer is one of the deadliest diseases. It is affecting majority of women world wide. Computer Aided Diagnosis (CAD) systems can be used to help radiologists in order to examine the initial symptoms. One of the early symptoms is micro-calcifications. Detection of abnormalities is an essential part of treatment in the right direction. Along with detection of abnormalities, the classification of micro-calcification has a vital importance. Timely detection and classification of micro-calcification as malignant or benign can save a lot of women. We have used region based convolutional neural networks and obtained 92.7% mean average precision at training time while at testing time mAP is 89.2%.

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Adeela Islam and Zobia Suhail "Automatic classification and detection of abnormalities in mammograms using deep learning", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860V (13 July 2022); https://doi.org/10.1117/12.2624216

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KEYWORDS

Mammography

Convolutional neural networks

Tumors

Image classification

Breast cancer

Neurons

Tumor growth modeling

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