Abstract
In current times, Diabetic retinopathy (DR) might be more difficult to diagnose when coexisting with glaucoma, since the two diseases share retinal abnormalities. Worldwide, DR is one of the most common causes of blindness. Conventional convolutional neural network (CNN)-based approaches struggle significantly with this type of co-morbid imaging due to the inherent difficulty in understanding both coarse-grained features and global correlations. The authors of this study propose a novel deep learning architecture, Vision Transformer (ViT) with Bi-Directional Feature Fusion (BFF) (ViT-BiFusionDRNet-HGS), to address these limitations. It is fine-tuned using the HGS technique, which was created for the Hunger Games, and combines a Vision Transformer (ViT) with Bi-Directional Feature Fusion (BFF). The BFF module enables the learning of semantic features from low-level textures, while the Vision Transformer captures long-distance spatial correlations. By incorporating the Hunger Games Search (HGS) algorithm into the model, it optimizes crucial hyperparameters and fusion weights, allowing for better generalization across complex fundus images, faster convergence, and more accurate lesion localization. With a classification accuracy of 98.4% and sensitivity levels higher than those of CNN, standalone ViT, and other baseline optimizers, the model demonstrated superior performance on open-source datasets for diabetic retinopathy and glaucoma fundus images. Clinically, ViT-BiFusionDRNet-HGS shows great potential as a real-time, scalable system for automated analysis of retinal abnormalities in complex diagnostic situations.