Author(s):
1. Anjum Abbas:
Federal Directorate of Education Islamabad, Pakistan.
Abstract:
Diabetic Retinopathy (DR) is a leading cause of vision impairment worldwide, necessitating early detection and accurate classification for effective intervention. Traditional machine learning (ML) models have demonstrated significant success in DR detection however, they face challenges related to computational efficiency, feature extraction, and interpretability. Quantum Machine Learning (QML) offers a promising alternative by leveraging quantum computing principles to enhance pattern recognition, optimize feature selection, and improve diagnostic accuracy. This paper explores the application of QML in DR diagnosis, focusing on quantum enhanced deep learning models and hybrid quantum classical frameworks. By utilizing algorithms such as Quantum Support Vector Machines (QSVMs) and Variational Quantum Circuits (VQCs), we investigate improvements in classification performance and computational speed. Experimental results suggest that QML models outperform classical counterparts in specific scenarios, demonstrating potential for early and more precise DR detection. This research highlights the role of Quantum Computing in medical imaging. It encourages further exploration of QMLbased solutions for ophthalmic diseases.
Page(s):
97-97
DOI:
DOI not available
Published:
Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
Diabetic retinopathy
,
Medical imaging
,
Early Disease Detection
,
Hybrid Quantum Classical Model
,
Quantum Support Vector Machine
,
Quantum Machine Learning
,
Variational Quantum Circuit
References:
References are not available for this document.
Citations
Citations are not available for this document.