COVID-ECG-RSNet: COVID-19 classification from ECG images using swish-based improved ResNet model

Abstract

Millions of humansworldwide have been impacted by consecutive COVID-19waves during the past threeyears. The correct treatment for COVID-19 has not yet been identified because vaccine receivers have also contracted the illness. People’s lives can be saved, and patients can be shielded from laborious treatments with the quick and accurate diagnosis of coronavirus. Investigators have utilized various clinical imaging modalities i.e.,CT-Scan and X-ray for the identification of coronavirus, while, less emphasis is spent on the utilization ofECG images in recognizing the COVID-19-affected patients. In contrast to CT-Scan and X-ray images, ECG samples arereadily accessible, hence we utilize them to diagnose coronavirus. Since scientists generally transform the ECG data into integer representation before using any approach, which indicates higher computing overhead. Accurateand quickcoronavirus identification from the ECG modalityis a difficult and cumbersome operation. In this study, we attempted to address these issues by leveraging the ECG filesdirectly in an improved deep learning (DL)methodology called the COVID-ECG-RSNet. Descriptively, a novel DL model is presented by altering the ResNet-50 model by introducing an optimized activation approach called the Swish method during the feature engineering process. Moreover, we introduced multiple dense layers at the end of the proposed CNN structure to ensure more robust sample features for classification purposes. The introduced COVID-ECG-RSNet model is proficient in categorizing the ECG samples as being normal, coronavirus, myocardial infarction (MI), abnormal heartbeats (AHB), and victims with Previous history of Myocardial Infarction (PMI) groups. A vast evaluation with the help of a complicated and publically accessible data sample of ECG images is accomplished to show the effectiveness of our approach in recognizing the coronavirus images in contrast to other categories of heart diseases and healthy samples via using the ECG modality. We have attained an accuracy score of 98.8%, along with precision, and recall scores of 98.9%, and 98.7% respectively indicating the efficacy of suggested approach.