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Diabetic Retinopathy (DR) is a critical source of blindness that can be prevented globally, and accurate analysis of retinal fundus images enables early detection. Fundus images are often affected by multiple noise sources, which impair image quality and hinder the observation of delicate retinal structures, including microaneurysms and small blood vessels. Deep learning driven denoising models are computationally intensive and prone to overfitting on small medical datasets. In order to overcome these shortcomings, the present paper suggests a Quantum Denoising Autoencoder (QDAE), a hybrid quantum-classical architecture, which uses convolutional feature coding with parameterized quantum circuits (PQCs) in latent space. The suggested QDAE applies quantum superposition and entanglement to improve the latent representations, thereby improving denoising and retinal detail preservation. Experiments on the Diabetic Retinopathy 224 × 224 (2019) dataset show that QDAE performs considerably better than classical denoising architectures, including CAE, ResNet, and DnCNN with PSNR of 38.8 dB, SSIM of 0.96, and AMI of 0.88. The approach preserves delicate retinal patterns and intensity consistency, while incurring a slight computational overhead associated with shallow quantum circuits. The results presented above demonstrate that QDAE is a potential quantum-aided architecture for denoising retinal images and a feasible preprocessing procedure in early diabetic retinopathy.
Chilukuri et al. (Wed,) studied this question.