Early retinal disease detection from fundus images using deep neural networks.

Manual diagnosis of retinal diseases from fundus images is slow, subjective, and highly dependent on specialist availability, an ongoing challenge in low-resource regions that leads to delayed treatment and preventable vision loss. This study aims to develop a high-accuracy, automated deep learning framework for early detection of multiple retinal diseases using fundus images, with emphasis on methodological transparency, comparative model evaluation, and applicability in real clinical environments. A dataset of 3,848 labeled fundus images from the University of Gondar Referral Hospital was preprocessed using histogram equalization, noise reduction, normalization, and targeted augmentation to address class imbalance. Five pre-trained CNN architectures, VGG19, ResNet50, InceptionV3, MobileNetV2, and DenseNet201, were fine-tuned using transfer learning. Models were evaluated using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices. DenseNet201 achieved the best performance with 92.78% accuracy, demonstrating balanced and superior class-wise detection: F1-scores of 0.98 (Normal), 0.92 (Glaucoma), 0.92 (DR), and 0.86 (AMD). The confusion matrix showed minimal misclassification, with high recall for the underrepresented AMD class (0.91). DenseNet201 consistently outperformed all competing models. The study validates DenseNet201 as a robust, high-accuracy model for early multi-disease retinal screening on a novel, region-specific dataset. The proposed framework offers a scalable, automated diagnostic tool capable of reducing clinical workload and enhancing screening coverage in underserved settings.