Comprehensive Modality Integration for Diabetic Retinopathy Image Analysis

Diabetic retinopathy (DR) is frequently discovered in the eyes of diabetics and this has been found to be a contributing factor to vision loss. Early intervention, along with regular fundus photography monitoring, is the most effective way to treat the condition. There are many patients with diabetes who need intensive screening; hence, there is an increase in computer-assisted and fully automated methods for diagnosing DR. Neural networks have come a long way in recent years in various domains. As a result of automating diagnosis of DR and giving customized suggestions to DR patients, it may be seen that precise as well as intricate DR classification is important. Here we have showed cross-modality feature fusion framework for diabetic retinopathy (DR) images categorization. Cross mode here means an RGB image having green channel. A multi-scale and multi-receptive feature extraction block has thus been presented initially so as to learn local and global features from both modalities. Moreover, the learnt features at different scales are successfully fused with current multi-level feature fusion block for image classification task. Our existing system has been compared against state-of-the-art (SOTA) deep learning frameworks for categorizing DR images using the MESSIDOR and IDRID databases. Results analysis shows clearly that the ongoing cross-modality feature fusion based classification framework outperforms all the present SOTA frameworks according to some evaluation metrics.