ECGNet-ViT: Hybridizing GoogleNet with Vision Transformer for Accurate COVID-19 Detection from ECG Images

Abstract

COVID-19 has affected millions of people around the world in the last three years. Despite widespread vaccination efforts, infections persist and definitive treatments remain elusive. Therefore, early and accurate detection of COVID-19 is critical to minimize invasive procedures and reduce mortality. Although radiographs and CT scans are commonly used for the diagnosis of COVID-19, electrocardiogram (ECG) images remain underutilized despite their widespread availability. This limited use can be attributed to the complex transformations required to process ECG data, which increase computational demands. This study proposes a novel hybrid deep learning model ECGNet-ViT for COVID-19 detection. The model combines the multi-scale feature extraction capabilities of GoogleNet (GNet) with Swish activation functions and densely connected layers, and then integrates it with Vision Transformer (ViT) to effectively capture long-range dependencies in classification tasks. This approach can efficiently analyze ECG data and accurately classify samples into five categories: normal, COVID-19, myocardial infarction (MI), previous myocardial infarction (PMI) and arrhythmia (AHB). Comprehensive experiments on a publicly available ECG datasets demonstrate the effectiveness of the proposed model, achieving 99.13% accuracy, 99.19% precision, 99.24% recall, and 99.22% F1 score. These results highlight the potential of the proposed model to provide reliable, non-invasive support in COVID-19 diagnosis based on ECG data.