Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 22(01), 055-060
Received on 22 February 2024; revised on 28 March 2024; accepted on 31 March 2024
Traditional convolutional neural networks (CNNs) have shown potential for recognizing retinopathy caused by diabetes (DR). However, developing quantum computing has the possibility for improved feature representation. We propose a hybrid approach that combines classical CNNs with quantum circuits to capitalize on both classical and quantum information for DR classification. Using the Keras and Qiskit frameworks, our model encodes picture features into quantum states, allowing for richer representations. Through experiments on a collection of retinal pictures, our model displays competitive performance, with excellent reliability and precision in categorizing DR severity levels. This combination of classical and quantum paradigms offers a fresh approach to enhancing DR diagnosis and therapy.
Diabetic Retinopathy; Hybrid Model; Quantum Computing; Convolutional Neural Networks; Quantum Circuit; Image Classification.
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M. Mounika, L. Sahithi, K. Prasanna Lakshmi, K. Praveenya and N. Ashok Kumar. Quantum driven deep learning for enhanced diabetic retinopathy detection. World Journal of Advanced Research and Reviews, 2024, 22(1), 055-060. Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.0964