CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis

Abstract

In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.