Diagnosing retinal diseases is a fundamental challenge in developing robust multi-disease classification systems due to the limited availability of datasets and inconsistent quality. Therefore, this study presents \(\text{DeepRetina}\) , a framework addressing these challenges through dataset harmonization. Five distinct retinal image datasets were unified using systematic preprocessing, resulting in a consolidated dataset of 29,966 high-resolution fundus images across eight disease categories. Harmonization corrects variations in image quality, color, and lighting resulting from different imaging devices or conditions. Data harmonization enhances the model’s ability to generalize across diverse datasets by standardizing the color and texture properties of images. The study compares the performance of custom CNN, EfficientNetV2, and MobileNetV3Large architectures for multi-disease classification. EfficientNetV2 achieved the highest accuracy of 79% with a precision of 54%. The proposed methodology significantly advances the field by (1) establishing a robust approach for harmonizing heterogeneous datasets, (2) presenting a large-scale, unified dataset for future research, and (3) presenting a comparative analysis of deep learning architectures optimized for retinal disease classification. \(\text{DeepRetina}\) lays the foundation for scalable and accurate automated retinal disease diagnosis, contributing to improved detection and classification in ophthalmology.
