Hybrid Quantum Convolutional Neural Networks for Enhanced Diabetic Retinopathy Detection
Abstract
Diabetic Retinopathy (DR) is a severe complication of diabetes, characterized by damage to the blood vessels in the retina, leading to an increased risk of vision loss or blindness. Patients with diabetes who develop DR face a significant risk of visual impairment, underscoring the need for advanced and reliable diagnostic tools. This research addresses this need by introducing a hybrid Quantum Convolutional Neural Network (QCNN) model, designed specifically for the automated detection of DR. By leveraging quantum principles and classical deep learning, this model provides a powerful approach to screening for diabetic retinopathy. In a Convolutional Neural Network (CNN), convolutional layers are primarily responsible for feature extraction from images, capturing intricate details essential for accurate classification. However, the proposed hybrid QCNN introduces quantum layers in addition to traditional convolutional layers, combining the strengths of both quantum computing and classical neural network structures. Quantum computing principles, such as superposition and entanglement, allow the QCNN to encode and process information in unique ways, enhancing the efficiency of feature extraction and the overall performance of the model. Superposition enables the QCNN to represent and manipulate multiple states simultaneously, while entanglement allows for a more complex, interconnected representation of image features. These quantum properties enable the QCNN to explore a…
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