Optimized EfficientNet-B3 for Multiclass Diabetic Retinopathy Detection: A Deep Learning Framework with Hybrid Fine-Tuning and Confidence-Aware Inference

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, and early detection is critical for effective treatment. However, automated multiclass DR grading remains challenging due to subtle inter-stage differences, image quality variations, and class imbalance in retinal fundus datasets. This paper proposes an optimized EfficientNet-B3 framework that integrates adaptive augmentation, staged progressive fine-tuning, hybrid optimization, and confidence-aware inference to address these challenges. The proposed framework employs a three-stage optimization strategy: (1) warm-up training of the classifier head only; (2) progressive unfreezing of MBConv blocks; and (3) full-network fine-tuning with AdamW optimizer, cosine learning rate decay, label smoothing, and mixed-precision training. A class-balanced sampling strategy with adaptive augmentation mitigates data imbalance. Confidence-aware inference combines maximum softmax probability with entropy-based uncertainty estimation for improved clinical interpretability. We evaluated the framework on a combined dataset of 92,501 retinal images from EyePACS, APTOS, APTOS-Gaussian filtered, and Messidor, using a balanced subset of 12,580 images for controlled experimentation (70/15/15 train/validation/test split). The proposed model achieved 99.00% accuracy, 98.64% precision, 98.80% recall, 98.72% F1-score, and 98.98% specificity for five-class DR grading, outperforming baseline ResNet-152 (91.96% accuracy) and standard EfficientNet-B3 (95.94% accuracy). The area under the ROC curve reached 1.00. The model requires approximately 12 million parameters and achieves inference times of a few milliseconds per image on GPU hardware. These results demonstrate that the proposed optimization strategies significantly improve multiclass DR grading performance while maintaining computational efficiency, suggesting practical viability for large-scale retinal screening programs and teleophthalmology applications.