Detection of COVID-19 in Chest X-Ray Images Using a CNN Model toward Medical Applications.

Since the unexpected upsurge of the severe acute respiratory syndrome coronavirus 2 (COVID-19) in 2019, it has rapidly spread all over the world. This viral pandemic has dramatically affected human health and daily life. Recent advances in artificial intelligence (AI) techniques are considered key enablers to expediting COVID-19 detection issues. In particular, the development of high-performance deep learning (DL) models with high levels of accuracy is a significant step towards a fast and high-precision method for the detection and diagnosis of COVID-19 infected patients. This paper proposes a convolutional neural network (CNN) model for the classification of COVID-19 positive infected and negative/normal patients. This model is applied to a dataset consisting of 3,000 chest X-ray images in 2 classes of diagnoses– COVID-19 and normal. The CNN is implemented via a Keras framework with a hyperparameter tuning technique, and a data augmentation technique is performed to achieve the best accuracy. Experimentally, the confusion matrix, precision-recall curve, and receiver operating characteristic curve (ROCC) are utilized to analyze the performance of the CNN model. Experimental results demonstrate that the CNN provides a high-performance classification of COVID-19 patients with a testing accuracy of 99% and a testing loss rate of 0.034. The Precision, Recall, F1-score, and the area under the ROCC for this model are 99.02%, 98.97%, 99%, and 100%, respectively. This model applied to X-ray images provides a quick and accurate approach to distinguishing between normal negative cases and COVID-19 infected patients, and should aid doctors and radiologists in the screening of COVID-19 patients. [ABSTRACT FROM AUTHOR]