Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images
This work provides an incremental improvement in COVID-19 diagnosis for medical professionals by offering a potentially more accurate prediction model using chest X-ray images.
This paper developed a hybrid quantum-classical convolutional neural network (HQCNN) model to predict COVID-19 from chest X-ray images. The model achieved high performance, with accuracies of 98.4% and 99% on two different COVID-19 vs. normal/pneumonia datasets, and 88.6% on a multi-class dataset.
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.