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

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. [ABSTRACT FROM AUTHOR]