Convolution neural network based covid-19 screening model

Abstract

Coronavirus Disease 2019 (COVID-19) is a high death rate respiratory condition that requires easy-to-reach markers for prediction. The electrocardiograph (ECG) alterations that may occur after COVID-19 hospitalization have not been fully studied yet. COVID-19 also affects heart function, which can be seen on an ECG. As a result, ECG can be used to detect virus-infected individuals. The database consists of ECG images. In this scenario, a convolution neural network (CNN) is utilized to classify COVID-19 ECG. The model is made up of eight layers, including a convolution layer, a max-pooling layer and a dense layer. The ECG image is fed into a CNN model, which classifies the COVID-19 ECG. The model provides us with 98.11% accuracy, 98.6% sensitivity and 96.40% specificity. Although 100.00% of the categorization of normal images and COVID-19 ECGs were not accurately determined by the proposed CNN model, this is the first CNN model to categorize ECG images into normal and COVID-19 classes from the ECG database and provide additional diagnostic to medical experts. © 2021 ASSA.