A Hybrid Machine Learning Algorithm for Glaucoma Detection Using Angle Encoding and Quantum Entanglement
Abstract
Glaucoma is a progressive optic neuropathy that leads to irreversible vision loss if not detected early. Automated analysis of retinal images plays an important role in detecting glaucoma-related structural abnormalities; however, conventional image processing and classical machine learning methods often struggle to model complex nonlinear relationships among clinical biomarkers. This study presents a quantum enhanced diagnostic framework for glaucoma detection that encodes retinal structural features into quantum states and analyzes them using quantum machine learning techniques. Optical Coherence Tomography (OCT) provides high-resolution cross-sectional visualization of retinal layers and facilitates quantitative assessment of key biomarkers, including Retinal Nerve Fiber Layer (RNFL) thickness, Ganglion Cell-Inner Plexiform Layer (GCIPL) thickness, and Bruch's Membrane Opening-Minimum Rim Width (BMO-MRW). After normalization, the features are transformed into quantum states via angle encoding and subsequently processed within an entangled quantum circuit. Classification is performed using a Quantum Support Vector Machine (QSVM) with a fidelity-based quantum kernel. Experimental evaluation shows that the proposed model achieves a classification accuracy of 73% on the dataset that shows promising results over classical baseline SVM model. Although classical deep learning models often report higher accuracies, this work demonstrates the feasibility of representing clinically…
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