DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images

The analysis of histopathological images is the core way for detecting breast cancer, the most insidious type of cancer for women. Artificial intelligence-based applications are used as an effective and supportive tool for automated breast cancer detection. Especially, deep learning models are among the most popular approaches due to their high performances in classification problems of medical images. In this study, a novel and robust approach, based on the convolutional-LSTM (CLSTM) learning model, the pre-processing technique using marker-controlled watershed segmentation algorithm (MWSA), and the optimized SVM classifier, was proposed for detecting breast cancer automatically from histopathological images (HPIs). The CLSTM model trained on the BreakHis dataset, which is popular in the research community, composes of binary and eight-class classification tasks. The classification performance of the CLSTM model was significantly increased by using the processed HPIs with MWSA. For binary and eight-class classification tasks, the best scores were obtained by using the optimized SVM classifier with Bayesian optimization instead of the softmax classifier of the CLSTM model. The proposed approach, which provided very high performance for both classification tasks, was compared to the existing approaches using the BreakHis dataset. (C) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Automated summary

The analysis of histopathological images is crucial for detecting the most insidious type of cancer for women, breast cancer. Artificial intelligence-based applications, particularly deep learning models, are effective tools for automated breast cancer detection due to their high performance in medical image classification. In this study, a novel approach using CLSTM model, MWSA pre-processing technique, and optimized SVM classifier showed significant performance improvements in binary and eight-class classification tasks for detecting breast cancer from histopathological images.