Location-aware convolutional neural networks based breast tumor detection

Breast cancer is one of the most common types of cancer affecting the lives of millions. Early detection and localization of the breast cancer tissues are vital for prevention and cure. Recently, there have been a number of developments on this front, particularly in the direction of automated image analysis. Although they are instrumental in expediting the process, such approaches lack the localization information and hence still demand substantial involvement of clinicians to deliver conclusive results. In this paper, we propose a novel approach for detecting and localizing cancer tissues from mammograms. In particular, we rely on Convolutional Neural Networks for exploiting the spatial relationship of the cancer tissues for detection and localization. Our evaluations on real datasets show that the proposed method is able to classify normal and tumor tissues with the classification accuracy of 90.8%. Furthermore, our approach achieves the sensitivity of 86.1% in detection with 1.4 false positives per image on the localization. In comparison to the state-of-the-art approaches, our method offers an additional 1.1% sensitivity improvement, along with reduced two false positives per image.