Reliability-aware fusion of ocular thermal imaging and radiometric features for proof-of-concept classification of diabetic retinopathy vs. healthy controls
Introduction Early detection of diabetic retinopathy (DR) is crucial for preventing permanent vision impairment, yet widely used screening techniques such as fundus photography and optical coherence tomography (OCT) depend on expensive equipment and expert clinical interpretation, restricting their use in large-scale and low-resource environments. To overcome these challenges, this study introduces ReliaFusion-Net, a reliability-aware multimodal framework for proof-of-concept binary classification of eyes with DR vs. healthy controls, using thermal eye images and radiometric temperature features. Methods The proposed ReliaFusion-Net architecture combines bidirectional co-attention with feature-wise linear modulation to effectively model complementary relationships between thermal image features and physiological temperature descriptors. A clinically curated dataset containing 558 thermal eye images, including 278 normal and 280 diabetic samples, was collected with ophthalmologist support. The framework was trained and evaluated for binary classification, and cross-validation experiments were conducted to assess model generalization. Results Using an EdgeNeXt backbone, the proposed model achieved 93.18% accuracy, 93.26% F1-score, and 0.9785 AUC on the test set. Cross-validation results further confirmed strong generalization performance. These findings indicate that the reliability-aware multimodal fusion strategy effectively discriminates eyes with DR from healthy controls using non-invasive thermal imaging data. Discussion By dynamically adapting modality contributions based on reliability, ReliaFusion-Net overcomes limitations associated with traditional fixed fusion strategies and improves classification robustness. Overall, the findings demonstrate proof-of-concept of reliability-aware multimodal learning to distinguish eyes with DR from healthy controls using non-invasive thermal imaging, with potential applicability in tele-ophthalmology settings. Future work focuses on multi-stage disease classification and longitudinal disease progression analysis.