Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification

Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and treatment to avoid permanent loss of vision. The computer-aided design could ease this screening process, which requires limited time, and assist clinicians. The main complexity in classifying images involves huge computation, leading to slow classification. Certain image classification approaches integrating quantum computing have recently evolved to resolve this. With its parallel computing ability, quantum computing could assist in effective classification. The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score.