Deep Learning-based Mammogram Classification for Breast Cancer

Abstract

Deep Learning (DL) is a rising field of researches in the last decade by exposing a hybrid analysis procedure including advanced level image processing and many efficient supervised classifiers. Robustness of the DL algorithms to the big data enhances the analysis capabilities of machine learning models by feature learning on heterogeneous image database. In this paper, Convolutional Neural Network (CNN) architecture was proposed on simplified feature learning and fine-tuned classifier model to separate cancer-normal cases on mammograms. Breast Cancer is a prevalent and mortal disease that appeared resultant mutating of normal tissue into tumor pathology. Mammograms are common and effective tools for the diagnosis of breast cancer. DL-based computer-assisted systems have the capability of detailed analysis for even small pathology that may lead the curing progress for a complete assessment. The proposed DL-based model aimed at assessing the applicability of various feature-learning models and enhancing the learning capacity of the DL models for an operative breast cancer diagnosis using CNN. The mammograms were fed into the DL to evaluate the classification performances in accordance with various CNN architectures. The proposed Deep model achieved high classification performance rates of 92.84%, 95.30%, and 96.72% for accuracy, sensitivity, specificity, and precision, respectively.