COVID-19 X-Ray Image Classification Using Deep Convolution Neural Network

ABSTRACT

The dramatic rise of the novel coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease diagnosis. The COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection, especially during the flu season. Early studies identified abnormalities in chest X-ray images of COVID-19-infected patients that could be beneficial for disease diagnosis. Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep convolutional neural network (DCNN) architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. In this study, DCNN models cater to image classification. We used publicly available resources of 13,377 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. The experimental results have shown the overall accuracy as high as 97.5% which demonstrates the good capability of the proposed DCNN model in the current application domain. Comparative results in terms of accuracy, and error rate between the networks are presented. The excremental result show that our proposed methods is efficient and effective. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.