Enhancing Breast Cancer Histopathological Image Classification using Attention-Based High Order Covariance Pooling

Abstract

The type of cancer that affects female patients most frequently is breast cancer. Computer-aided diagnosis, these days, proves to be helpful for many diseases including breast cancer. Deep learning based approaches have yielded encouraging results in many computer vision applications. In the case of breast cancer histopathological image classification, many methods have been proposed but the accuracy is still insufficient for full deployment. Due to this reason, we propose a deep learning based approach, DHA-Net which consists of an attention module and a high-order pooling layer incorporated into a ResNet-18 architecture. DHA-Net was applied to three well-known datasets in the field of breast cancer histopathological image classification, i.e. BreakHis, BACH2018 and a closely related Kaggle-Breast cancer histopathology dataset. In this area of medical imaging, experiments have shown that DHA-Net works better than other existing techniques.