Quantum neural network application for exudate affected retinal image patch identification Online publication date: Mon, 04-Jul-2022
by Mahua Nandy Pal; Minakshi Banerjee; Ankit Sarkar
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 4, 2022
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.
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