DMS-Net:Dual-Modal Multi-Scale Siamese Network for Binocular Fundus Image Classification

• URL: http://arxiv.org/abs/2504.18046v1
• Date: Fri, 25 Apr 2025 03:27:28 GMT
• Title: DMS-Net:Dual-Modal Multi-Scale Siamese Network for Binocular Fundus Image Classification
• Authors: Guohao Huo, Zibo Lin, Zitong Wang, Ruiting Dai, Hao Tang,
• Abstract summary: Ophthalmic diseases pose a significant global health challenge, yet traditional diagnosis methods often fail to account for binocular pathological correlations.<n>We propose DMS-Net, a dual-modal multi-scale Siamese network for binocular fundus image classification.<n>Our framework leverages weight-shared Siamese ResNet-152 backbones to extract deep semantic features from paired fundus images.
• Score: 8.010725085988296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ophthalmic diseases pose a significant global health challenge, yet traditional diagnosis methods and existing single-eye deep learning approaches often fail to account for binocular pathological correlations. To address this, we propose DMS-Net, a dual-modal multi-scale Siamese network for binocular fundus image classification. Our framework leverages weight-shared Siamese ResNet-152 backbones to extract deep semantic features from paired fundus images. To tackle challenges such as lesion boundary ambiguity and scattered pathological distributions, we introduce a Multi-Scale Context-Aware Module (MSCAM) that integrates adaptive pooling and attention mechanisms for multi-resolution feature aggregation. Additionally, a Dual-Modal Feature Fusion (DMFF) module enhances cross-modal interaction through spatial-semantic recalibration and bidirectional attention, effectively combining global context and local edge features. Evaluated on the ODIR-5K dataset, DMS-Net achieves state-of-the-art performance with 80.5% accuracy, 86.1% recall, and 83.8% Cohen's kappa, demonstrating superior capability in detecting symmetric pathologies and advancing clinical decision-making for ocular diseases.

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