AWFCNET: An Attention-Aware Deep Learning Network with Fusion Classifier for Breast Cancer Classification Using Enhanced Mammograms

Abstract

Breast cancer remains a significant public health concern and a leading cause of female mortality despite recent advances in healthcare. Experts agree that its early prognosis is a key to survivability. In this research, we proposed a deep learning architecture code-named AWFCNET. It comprised multiple segments of preprocessing techniques (color shifting & image enhancement), feature learning based on ResNeXt-101 convolutional network as a backbone with transfer and attention-aware mechanisms, and fusion classifier composed of three recurrent neural networks. The generalization capability of the pipeline produced 98.10% accuracy on a mammogram dataset using 10-fold cross-validation. Computational benchmarks revealed that it surpassed existing state-of-the-art approaches with provisions of visual interpretability via gradient maps. Thus, our framework could complement physicians’ expertise in rapid and dependable breast cancer diagnoses.