Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion

Abstract

This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and similarity among different classes, MSFF enhances the global context perception in the window partitioning framework through Context Modulation Attention (CMA). Meanwhile, it extracts fine-grained local information via the multi-stage Contextual Information Refinement (CIR) module and gradually fuses multi-stage local and global features to generate richer semantic representations. The experimental results demonstrate that MSFF significantly outperforms existing methods in multiple performance metrics (including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Kappa coefficient, Area Under the Curve (AUC), balanced accuracy, and geometric mean) on four datasets (Endoscopic Bladder Tissue, Kvasir, SARS-COV-2 Ct-Scan, and Thyroid Nodule), showing its excellent performance in the task of medical image classification.

Keywords

• Medical images
• global semantics
• local features
• transformer
• context modulated attention
• multi-stage feature fusion network