  The reliable and rapid identification of the COVID-19 has become crucial to
prevent the rapid spread of the disease, ease lockdown restrictions and reduce
pressure on public health infrastructures. Recently, several methods and
techniques have been proposed to detect the SARS-CoV-2 virus using different
images and data. However, this is the first study that will explore the
possibility of using deep convolutional neural network (CNN) models to detect
COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and
other cardiovascular diseases (CVDs) were detected using deep-learning
techniques. A public dataset of ECG images consists of 1937 images from five
distinct categories, such as Normal, COVID-19, myocardial infarction (MI),
abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used
in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101,
InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three
different classification schemes: two-class classification (Normal vs
COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and
finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI). For
two-class and three-class classification, Densenet201 outperforms other
networks with an accuracy of 99.1%, and 97.36%, respectively; while for the
five-class classification, InceptionV3 outperforms others with an accuracy of
97.83%. ScoreCAM visualization confirms that the networks are learning from the
relevant area of the trace images. Since the proposed method uses ECG trace
images which can be captured by smartphones and are readily available
facilities in low-resources countries, this study will help in faster
computer-aided diagnosis of COVID-19 and other cardiac abnormalities.
