Hybrid quantum-classical deep learning framework for balanced multiclass diabetic retinopathy classification

Abstract

Diabetic Retinopathy (DR) is a progressive eye disease and a leading cause of preventable blindness among diabetic patients. Early and accurate classification of its severity stages is crucial for effective treatment but remains challenging due to class imbalance, high-resolution data, and limited scalability of existing models. This study presents a novel hybrid quantum-classical deep learning framework to address these limitations in five-class DR classification. The model achieves a balanced accuracy of 80.96 % on the APTOS 2019 dataset, outperforming several classical baselines across all DR stages. It is optimized for computational efficiency and class-balanced learning, making it suitable for deployment in telemedicine platforms and low-resource clinical settings. This work contributes a scalable AI-based diagnostic approach that fuses deep learning with emerging quantum computing techniques. The methodology, results, and publicly shared codebase provide a replicable framework for researchers and practitioners working in AI for medical imaging and early disease screening. This method is well-suited for low-resource clinical environments and tele-ophthalmology applications. The method involves an:

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  ResNet-50 feature extractor with a 4-stage dense projection (2048→8) for quantum-ready compression
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  8-qubit VQC with parameterized RY–RZ gates and ring-style entanglement for high expressiveness
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  Stratified sampling + mixed-precision training for efficiency and class-balanced generalization