A contrastive enhancement and context guided fusion network for carotid plaque classification

Carotid ultrasound image classification plays a vital role in carotid plaque diagnosis and stroke risk prediction. However, mixed-echoic plaques are difficult to classify because of low contrast between plaques and surrounding tissues and insufficient global contextual modeling. To address these issues, this paper proposes a Contrastive Enhancement and Context Guided Fusion Network (CEGF-Net) for carotid plaque classification. The proposed network contains a Contrast-Enhanced Feature Fusion (CEFF) module and a Context Guided Fusion (CGF) module. CEFF enhances the contrast between plaque foreground and surrounding tissue background, while CGF adaptively integrates local details and global contextual cues to improve the representation of ambiguous plaque regions. Experiments on 1270 carotid plaque ultrasound images collected from a collaborating hospital show that CEGF-Net improves classification accuracy by approximately 1.9% compared with the baseline network. In particular, the accuracy for mixed-echoic plaque classification increases by around 9%, demonstrating the effectiveness of the proposed approach.