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Diabetic Retinopathy Detection Using Classical-Quantum Transfer Learning Approach and Probability Model

Amna Mir1, Umer Yasin1, Salman Naeem Khan1, Atifa Athar3,*, Riffat Jabeen2, Sehrish Aslam1
1 Department of Physics, Comsats University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
2 Department of Statistics, Comsats University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
3 Department of Computer Science, Comsats University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
* Corresponding Author: Atifa Athar. Email:
(This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)

Computers, Materials & Continua 2022, 71(2), 3733-3746.

Received 10 August 2021; Accepted 19 October 2021; Issue published 07 December 2021


Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically. Thus, our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach. To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device, IBM Qiskit BasicAer device and Google Cirq Simulator device. Both models are built based on PyTorch machine learning library. We bring about a contrast performance rate between classical and quantum models. Our proposed model achieves an accuracy of 93%–96% on the quantum hybrid model and 85% accuracy rate on the classical model. So, quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.


Diabetic Retinopathy (DR); quantum transfer learning; inception-V3; variational quantum circuit; image classification

Cite This Article

A. Mir, U. Yasin, S. Naeem Khan, A. Athar, R. Jabeen et al., "Diabetic retinopathy detection using classical-quantum transfer learning approach and probability model," Computers, Materials & Continua, vol. 71, no.2, pp. 3733–3746, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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