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International Conference on Emerging Technologies and Future Innovations (ETFI)

Quantum-Classical Hybrid Model for Retinal Disease Classification with Interpretability

Lata Verma, Satyam Sase, Siddhiraj Yele, Prasad Kharate, Sandhya Gaikwad

Published Feb 12, 2026• 0 citations
Publisher
DOI
10.1109/etfi68128.2026.11484922

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Recent breakthroughs in medical computational modelling, particularly in ophthalmology, have produced remarkable achievements in disease identification and classification with the aid of deep learning and hybrid quantum–classical methods. Though the current ensemble CNNs have more than 99% accuracy in diagnosing glaucoma, diabetic retinopathy, and other retinal diseases, scalability and interpretability remain problematic. This paper presents a novel hybrid quantum–classical approach that combines quantum feature encoding with ensemble CNNs optimised to improve diagnostic accuracy and lower computational burden. The new model exhibits higher feature extraction efficiency and noise robustness than traditional architectures. Through the use of quantum state representations, the system enables better decision-making, prediction, and personalised therapy suggestions. Integrated automated analysis of behaviour in this system also supports real-time identification and adaptive treatment planning.In general, the main innovation is the integration of quantum encoding and ensembles of deep learning for the classification of ophthalmic disease, providing a scalable avenue toward intelligent, real-time healthcare analysis.