Breast Cancer Detection from Mammograms using Deep Learning

Abstract

Breast Cancer detection with mammography, which is a critical task in medical imaging serves as a primary screening tool. Deep Learning techniques have shown significant improvement in recent years for improving efficiency and accuracy in Breast Cancer detection from mammograms. Our project has proposed a deep leaning model for breast cancer detection using mammogram images by deploying Convolutional Neural Networks (CNN) for feature extraction and Image classification. The model has been trained on large number of datasets containing mammogram images which have been annotated by the expert Radiologists. Experimental results show the accuracy and effectiveness of our model by detecting abnormalities in the respective regions of mammogram images which are indicators of Breast Cancer. This method has shown significantly improved performance as compared to traditional methods of Breast cancer detection. The proposed methodology has shown importance in the medical field by aiding radiologists in early detection and diagnosis and ultimately improving patient outcomes.