Deciphering the Complexities of COVID-19-Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework

Abstract

The literature has widely described the interaction between cardiac complications and COVID-19. However, the diagnosis of cardiac complications caused by COVID-19 using Computed Tomography (CT) images remains a challenge due to the diverse clinical manifestations. To address this issue, this study proposes a novel configuration of Convolutional Neural Network (CNN) for detecting cardiac complications derived from COVID-19 using CT images. The main contribution of this work lies in the use of CNN techniques in combination with Long Short-Term Memory (LSTM) for cardiac complication detection. To explore two-class classification (COVID-19 without cardiac complications vs. COVID-19 with cardiac complications), 10 650 CT images were collected from COVID-19 patients with and without myocardial infarction, myocarditis, and arrhythmia. The information was annotated by two radiology specialists. A total of 0.926 was found to be the accuracy, 0.84 was the recall, 0.82 was the precision, 0.82 was the F₁-score, and 0.830 was the g-mean of the suggested model. These results show that the suggested approach can identify cardiac problems from COVID-19 in CT scans. Patients with COVID-19 may benefit from the proposed LSTM-CNN architecture's enhanced ability to identify cardiac problems.