A Study on Multi-class Classification of Breast Cancer Images using Ensemble Network and Transfer Learning

Background: Breast cancer causes millions of deaths all over the world every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increase the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi-class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. Methods: The current paper presents an ensemble Convolutional neural network for multi-class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from the publicly available BreakHis dataset and classified into 8 classes. Results: The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification. Conclusion: In this paper, an approach for multi-class classification on the breast images for cancer detection is proposed. The proposed architecture can be a viable option for the classification of histopathology images.