Quantum Machine Learning-Based Detection of Retinopathy in Retinal Images of Diabetic Patients

Diabetic retinopathy is one of the leading causes of vision loss worldwide, and early automated detection from retinal images is critical to timely treatment. This paper explores a quantum machine learning approach that integrates classical deep feature extraction and dimensionality reduction with a variational quantum circuit inspired by Grover’s algorithm. The model encodes retinal image features into a compact quantum representation and employs iterative quantum amplitude amplification to improve classification accuracy. Experiments conducted on the APTOS 2019 dataset demonstrate that this hybrid quantum-classical method achieves performance comparable to classical Support Vector Machine classifiers, a positive indicator given the early stage of quantum machine learning. These findings highlight the promise of quantum-enhanced techniques for advancing medical image analysis.