ArticleIntelligent systems with applications2022
OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique.
Article in Intelligent systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.Diagnostics (Basel, Switzerland) · 2024Review
- COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers.Sensors (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
COVID-19 is an infectious disease that has cost millions of lives all over the world. A faster and safer diagnosis of COVID-19 is highly desirable in order to stop its spread. An electrocardiogram (ECG) signal-based diagnosis has shown its potential in the diagnosis of cardiac, stroke, and COVID-19. In this study, an ensemble of three deep learning models are used for COVID-19 detection in ECG images for multi-class classification. The results obtained with the weighted average ensemble technique have been improved by using the grid search technique. For multi-class classification, an optimized weighted average ensemble (OWAE) model classifies the ECG images with an accuracy of 95.29%, an F1-score of 95.4%, a precision of 95.5%, and a recall of 95.3%. In case of binary classification, VGG-19, EfficientNet-B4, and DenseNet-121 performed comparatively well with 100% accuracy. These results show that deep learning can be used in the diagnosis of COVID-19 disease using ECG images.
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