Using Quantum circuits and feature maps to improve the efficiency of Classical Deep learning based classifier model.
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Brief Summary :-
Addressed the growing concern of diabetic retinopathy in India by devising an solution at the intersection of quantum computing and deep learning. Developed a quantum-enhanced deep convolutional neural network that integrates quantum computing's potential with image classification methods. This novel approach outperformed conventional techniques, achieving very High accuracy, precision, recall, specificity, and f1-score in diagnosing diabetic retinopathy severity levels using the Indian Diabetic Retinopathy Image Dataset (IDRID) over the classical deep learning based on CNN models.