Comparative Evaluation of EfficientNet-B3, Vision Transformers, and RETFound for Seven-Category Retinopathy Fundus Image Classification

Background/Objectives: Retinal fundus photography is the most widely available means of screening for sight-threatening disease, but it is difficult to read: several conditions leave overlapping signs on the same photograph, the features that separate them are often subtle and confined to small regions of the retina, and a single image carries no systemic context, so an automated reader must resolve categories on which experienced clinicians themselves agree only moderately. Methods: We compared five deep learning architectures—EfficientNet-B3, DenseNet-121, ViT-Base/16, Inception-V3, and RETFound ViT-Large—for simultaneous seven-class retinal disease classification on a multi-source fundus image benchmark, in a retrospective computational study using stratified five-fold cross-validation. We pooled 11,223 patient-eye fundus images from three publicly available, de-identified datasets (ODIR-5K, RFMiD, and APTOS 2019) spanning age-related macular degeneration (AMD), cataract, diabetic retinopathy (DR), glaucoma, hypertensive retinopathy, pathological myopia, and normal fundus; the cohort was markedly class-imbalanced (DR, 62.5%; normal, 25.6%; each remaining class < 3%), and no new patient data were collected. Images were deduplicated by MD5 hash and split at the patient level to eliminate data leakage, with a fixed 15% held-out test set and five stratified cross-validation folds; training images received Ben Graham illumination normalization and class-balancing augmentation, while validation and test images were unaugmented. Three ImageNet-pretrained CNNs (EfficientNet-B3, DenseNet-121, Inception-V3), ViT-Base/16, and RETFound ViT-Large (pretrained on 1.6 million retinal photographs via masked autoencoding) were trained; the primary outcome was the five-fold mean weighted-averaged F1 score, pairwise comparisons used paired t-tests on fold-level F1 (df= 4), and Grad-CAM was applied for explainability. Results: EfficientNet-B3 achieved the highest five-fold mean accuracy (95.53%; weighted-F1, 0.9507) and significantly outperformed RETFound ViT-Large (ΔF1=+0.0255; t=9.70; p<0.001), ViT-Base/16 (ΔF1=+0.0075; t=9.20; p<0.001), and Inception-V3 (ΔF1=+0.0103; t=8.38; p<0.001), while performing comparably to DenseNet-121 (t=2.24; p=0.089); Grad-CAM showed clinically plausible activation localization for the CNN architectures. Conclusions: A compact, resource-efficient CNN thus matched or exceeded a substantially larger retinal foundation model and Vision Transformers, indicating that larger domain-specific foundation models are not inherently superior for heterogeneous retinal screening; because such compact models run on commodity hardware, they may offer more practical screening solutions for community, rural, and resource-limited settings where ophthalmologist coverage is scarcest. External validity across imaging sources and populations remains unproven, underscoring that prospective, multicenter validation must precede clinical deployment. A provenance audit of this pooled cohort subsequently found it confounded. The audit identified five failure modes, three of them decisive: image width alone reproduced the diabetic retinopathy label at 99.98% accuracy using no retinal pixels, source and label were almost perfectly dependent with a Cramér’s V of 0.9996, and diabetic retinopathy recall was exactly 1.0000 for all five architectures. The cohort was rebuilt from a single source with per-eye labels, and nine architectures were retrained under one protocol with external validation. The corrected headline is a macro-AUC of 0.930 internally and 0.978 externally, with a macro-F1 of 0.686. No architecture is declared superior, and the pooled-cohort conclusion does not stand.