MFAN: Multi-Feature Attention Network for Breast Cancer Classification

Abstract

Cancer-related diseases are some of the major health hazards affecting individuals globally, especially breast cancer. Cases of breast cancer among women persist, and the early indicators of the diseases go unnoticed in many cases. Breast cancer can therefore be treated effectively if the detection is correctly conducted, and the cancer is classified at the preliminary stages. Yet, direct mammogram and ultrasound image diagnosis is a very intricate, time-consuming process, which can be best accomplished with the help of a professional. Manual diagnosis based on mammogram images can be cumbersome, and this often requires the input of professionals. Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. This paper presents a new Multi-Feature Attention Network (MFAN) for breast cancer classification that works well for small lesions and similar contexts. MFAN has two important modules: the McSCAM and the GLAM for Feature Fusion. During channel fusion, McSCAM can preserve the spatial characteristics and extract high-order statistical information, while the GLAM helps reduce the scale differences among the fused features. The global and local attention branches also help the network to effectively identify small lesion regions by obtaining global and local information. Based on the experimental results, the proposed MFAN is a powerful classification model that can classify breast cancer subtypes while providing a solution to the current problems in breast cancer diagnosis on two public datasets.