  With the periodic rise and fall of COVID-19 and numerous countries being
affected by its ramifications, there has been a tremendous amount of work that
has been done by scientists, researchers, and doctors all over the world.
Prompt intervention is keenly needed to tackle the unconscionable dissemination
of the disease. The implementation of Artificial Intelligence (AI) has made a
significant contribution to the digital health district by applying the
fundamentals of deep learning algorithms. In this study, a novel approach is
proposed to automatically diagnose the COVID-19 by the utilization of
Electrocardiogram (ECG) data with the integration of deep learning algorithms,
specifically the Convolutional Neural Network (CNN) models. Several CNN models
have been utilized in this proposed framework, including VGG16, VGG19,
InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has
outperformed the rest of the models, with an accuracy of 85.92%. Our results
show a relatively low accuracy in the rest of the models compared to the VGG16
model, which is due to the small size of the utilized dataset, in addition to
the exclusive utilization of the Grid search hyperparameters optimization
approach for the VGG16 model only. Moreover, our results are preparatory, and
there is a possibility to enhance the accuracy of all models by further
expanding the dataset and adapting a suitable hyperparameters optimization
technique.
