Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

Abstract

Despite the great efforts to find an effective way for COVID-19 prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via Chest X-Ray (CXR) images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN) model used the random quantum circuits (RQCs) as a base to detect COVID-19 patients with CXR images. A collection of 6952 CXR images, including 1161 COVID-19, 1575 normal, and 5216 pneumonia images, were used as a dataset in this work. The proposed HQCNN model achieved higher performance with an accuracy of 98.4\% and a sensitivity of 99.3\% on the first dataset cases. Besides, it obtained an accuracy of 99\% and a sensitivity of 99.7\% on the second dataset cases. Also, it achieved accuracy, and sensitivity of 88.6\%, and 88.7\%, respectively, on the third multi-class dataset cases. Furthermore, the HQCNN model outperforms various models in balanced accuracy, precision, F1-measure, and AUC-ROC score. The experimental results are achieved by the proposed model prove its ability in predicting positive COVID-19 cases.

References in corpus (18)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Supervised learning with quantum enhanced feature spaces
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• An overview of deep learning in medical imaging focusing on MRI
• Can AI help in screening Viral and COVID-19 pneumonia?
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Characterizing Quantum Supremacy in Near-Term Devices
• COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
• COVID-19 Image Data Collection: Prospective Predictions Are the Future
• Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
• Quantum machine learning: a classical perspective
• Recent progress in semantic image segmentation
• Quantum-enhanced machine learning
• Continuous-variable quantum neural networks
• GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
• Classification with Quantum Machine Learning: A Survey

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