- Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
- Vol: 12 Issue: 1
- Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms
Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms
Authors : Volkan Müjdat Tiryaki
Pages : 57-65
Doi:10.17798/bitlisfen.1190134
View : 8 | Download : 2
Publication Date : 2023-03-22
Article Type : Research
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.Keywords : Breast cancer, image classification, nodule, tumor, computer-aided diagnosis