Title: Quantum neural network application for exudate affected retinal image patch identification

Authors: Mahua Nandy Pal; Minakshi Banerjee; Ankit Sarkar

Addresses: CSE Department, MCKV Institute of Engineering, MAKAUT, Kolkata, West Bengal, India ' CSE Department, RCC Institute of Information Technology, MAKAUT, Kolkata, West Bengal, India ' CSE Department, MCKV Institute of Engineering, MAKAUT, Kolkata, West Bengal, India

Abstract: In the field of retinal disease identification, deep neural networks are exhaustively used. But the efficiency of quantum neural network in the field is not yet explored. Recently, quantum neural network achieved attention of researchers as it is required to explore if quantum network has any scope in the relevant field in terms of resource utilisation and decision-making during network learning. In this paper, efficiency of a simple quantum network model is experimented. In the present scenario, quantum classical models are unable to handle more than few qubits. Experimentally, it is found that the quantum neural network is quite efficient in representing the features of exudate affected retinal image patches. The accuracy of quantum neural net model is 84.28%. The accuracies are 51.80% and 88% respectively with comparable deep neural net and convolutional neural net models.

Keywords: quantum neural network model; deep neural network model; deep convolutional neural network model; retinal fundus image; exudates; classification; TensorFlow quantum; TFQ; parameterised quantum circuit; PQC.

DOI: 10.1504/IJCVR.2022.123847

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.4, pp.360 - 376

Received: 10 May 2021
Accepted: 21 May 2021

Published online: 04 Jul 2022 *

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