Exploring Dataset Variability in Diabetic Retinopathy Classification Using Transfer Learning Approaches

Diabetic retinopathy (DR) stands as a primary international cause of vision impairment that needs effective and swift diagnostic services to protect eye structures from advancing deterioration. The variations of imaging data that appear between sources create major obstacles for achieving consistent performance from models. The elimination of performance fluctuation problems during DR classifications across two benchmark datasets EYE-PACS and APTOS is examined through systematic transfer learning analysis using different high-performing CNN architectures including VGG16, VGG19, ResNet50, Xception, InceptionV3, MobileNetV2, and InceptionResNetV2. The research evaluates how data heterogeneity affects and how augmentation approaches impact the accuracy while stabilizing robustness in deep learning models. The research provides new insights through its extensive investigation of generalization performance based on dataset changes which utilize modified data augmentation methods for retinal images. A collection of data transformations such as rotation, flipping, zooming and brightness modifications create simulated realistic scenarios to handle imbalanced data classes. Academic research involved CNN pre-training followed by transfer learning on both databases while researchers evaluated the models through both untreated source data and augmented image testing procedures. InceptionResNetV2 outperformed its counterparts with 96.2% accuracy and Xception delivered 95.7% accuracy in APTOS evaluation and both models scored 95.9% and 95.4% respectively on EYE-PACS testing. When augmentation was applied it increased the performance level by 3% to 5% across all running models. The experimental outcomes demonstrate how adequate variable training allows these models to recognize datasets regardless of their heterogeneity. This analysis confirms that combining reliable deep learning structures with purposeful data enhancement techniques substantially enhances DR diagnosis reliability to build scalable future diagnostic solutions for ophthalmology practice.