A multimodal medical image fusion method based on a cross-modal channel-aware (CMCA) module

To address the problem of noise degrading fusion performance in medical images, we propose a multimodal medical image fusion method based on cross-modal channel-aware (CMCA) module. A dual-branch encoder enhanced with a squeeze-and-excitation module is introduced to apply channel-wise weighting during feature extraction and obtain modality-specific features. A cross-modal channel-aware fusion module is constructed to integrate the extracted information and achieve complementary feature fusion. A composite loss function combining image entropy and median-based weighting is adopted to preserve detail while suppressing noise during training, thereby enabling effective multimodal medical image fusion. Experimental results on an MRI-CT dataset show that the proposed method achieves an average gradient of 8.630, a standard deviation of 82.301 with a spatial frequency of 35.728, and a structural similarity index of 1.173, which makes it a valuable tool for assisting clinicians in lesion analysis.