International Journal of Computer Theory and Engineering

Abstract

Early and accurate detection of retinal diseases is crucial for preventing vision loss, yet traditional diagnostic methods remain limited by subjectivity and inefficiencies. This study introduces an Artificial Intelligence (AI)-driven diagnostic system leveraging hybrid deep learning models to detect Glaucoma, Macular Hole, Central Serous Retinopathy, and Drusen using fundus images. By integrating multiple architectures, including Residual Network (ResNet), Visual Geometry Group 16-layer network (VGG16), Densely Connected Convolutional Network (DenseNet), U-shaped Network (U-Net), and You Only Look Once version 8 (extra-large variant) (YOLOv8x), the system enhances diagnostic precision and generalization across diverse imaging conditions. Key innovations include the hybrid ResNet-VGG16 and DenseNet-VGG16 models, which significantly improve detection accuracy for Drusen and Central Serous Retinopathy, respectively. Additionally, the U-Net-ResNet hybrid architecture mitigates overfitting, ensuring more reliable Macular Hole detection, while the YOLOv8x object detection model outperforms traditional approaches in Glaucoma localization by accurately identifying the optic disc. These models, integrated into a web-based diagnostic platform, achieved sensitivities and specificities exceeding 95%, establishing a new performance benchmark for automated ophthalmic diagnostics. This research advances medical image analysis by demonstrating the efficacy of hybrid deep learning models, offering a scalable AI solution for early retinal disease detection. Its integration into clinical workflows highlights its potential to transform ophthalmic care, enhancing accessibility and improving patient outcomes.