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.
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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