Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

Abstract

The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning methods. A total of 3,360 patches were used from the Digital Database for Screening Mammography (DDSM) and CBIS-DDSM mammogram databases for convolutional neural network training and testing. Transfer learning was applied using Resnet50, Xception, NASNet, and EfficientNet-B7 network backbones. The best classification performance was achieved by the Xception network. On the original CBIS-DDSM test data, an AUC of 0.9317 was obtained for the five-way classification problem. The results are promising for the implementation of automated diagnosis of breast cancer.

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

“…Deep transfer learning techniques were used by TİRYAKİ et al. ( 32 ) to classify calcification diseases and breast cancer masses. Convolutional neural networks were trained and tested on a dataset of 3,360 patches taken from the CBIS-DDSM and (DDSM) mammography databases.…”