SwinCNN: An Integrated Swin Transformer and CNN for Improved Breast Cancer Grade Classification

Abstract

Breast cancer is the most commonly diagnosed cancer among women, globally. The occurrence and fatality rates are high for breast cancer compared to other types of cancer. The World Cancer report 2020 points out early detection and rapid treatment as the most efficient intervention to control this malignancy. Histopathological image analysis has great significance in early diagnosis of the disease. Our work has significant biological and medical potential for automatically processing different histopathology images to identify breast cancer and its corresponding grade. Unlike the existing models, we grade breast cancer by including both local and global features. The proposed model is a hybrid multi-class classification model using depth-wise separable convolutional networks and transformers, where both local and global features are considered. In order to resolve the self-attention module complexity in transformers patch merging is performed. The proposed model can classify pathological images of public breast cancer data sets into different categories. The model was evaluated on three publicly available datasets, like BACH, BreakHis and IDC. The accuracy of the proposed model is 97.800 % on the BACH dataset, 98.130 % on BreakHis dataset and 98.320 % for the IDC dataset.