Deep convolutional neural networks accurately predict breast cancer using mammograms

Abstract

Breast cancer in women is the most frequently diagnosed and major leading cause of cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail to diagnose breast cancer properly. In this research paper, we have employed a novel Deep Convolutional Neural Network (DCNN) model using a transfer learning strategy and compared the results with Machine Learning (ML) techniques such as Support vector machine (SVM) kernels and Decision Trees based on different features extracting strategies to distinguish cancer mammograms from normal subjects. In this study, we first extracted the hand-crafted features such as as texture, morphological, entropy-based, scale-invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) and fed into machine learning algorithm for classification. We then utilized the deep learning algorithms with transfer learning approach. The deep learning models yielded the highest detection performance with default and optimized parameters i.e. GoogleNet yielded accuracy (99.26%), AUC (0.9998) with default parameters and AlexNet yielded accuracy (99.26%), AUC (0.9996) with optimized parameters. The results reveal that proposed approach is more robust for early detection of breast mammograms which can be best utilized for improved diagnosis and prognosis.