Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method.

ABSTRACT: Breast cancer has now overtaken lung cancer as the world's most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909

SUBMITTER: Umer MJ 

PROVIDER: S-EPMC9146727 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature