A Convolutional Neural Network Based Patch Classifier Using Mammograms

Abstract

Breast Cancer is the most prevalent cancer among females worldwide. Early detection is key to good prognosis and mammography is the most widely-used technique, particularly in screening programs. However, mammography is a highly-skilled and often time-consuming task. Deep learning methods can facilitate the detection process and assist clinicians in disease diagnosis. There has been much research showing Deep Neural Networks’ successful use in medical imaging to predict early and accurate diagnosis. This paper proposes a patch-based Convolutional Neural Network (CNN) classification approach to classify patches (small sections) obtained from mammogram images into either benign or malignant cases. A novel patch extraction approach method, which we call Overlapping Patch Extraction, is developed and compared with the two different techniques, Non-Overlapping Patch Extraction, and a Region-Based-Extraction. Experimentation is conducted using images from the Curated Breast Imaging Subset of Digital Database for Screening Mammography. Five deep learning models, three configurations of EfficientNet-V2 (B0, B2, and L), ResNet-101, and MobileNet- V3L, are trained on the patches extracted using the discussed methods. Preliminary results indicate that the proposed patch extraction approach, Overlapping, produces a more robust patch dataset. Promising results are obtained using the Overlapping patch extraction technique trained on the EfficientNet-V2L model achieving an AUC of 0.90.