Authors:
Sara Khan
1
;
Walaa Ismail
2
;
Shada Alsalamah
3
;
Ebtesam Mohamed
4
and
Hessah A. Alsalamah
5
;
3
Affiliations:
1
Department of Information Systems and Technology Management, George Washington University, Washington, DC, U.S.A.
;
2
Management Information Systems Department, College of Business, Al Yamamah University, Riyadh, K.S.A.
;
3
Information Systems Department, King Saud University, Riyadh, K.S.A.
;
4
Faculty of Computer Science, Minia University, Minia, Egypt
;
5
Computer Engineering Department, College of Engineering and Architecture, Al Yamamah University, Riyadh, K.S.A.
Keyword(s):
Convolutional Neural Network (CNN), Covid-19, Data Imbalance, Electrocardiogram (ECG), Class Weights, VGG16.
Abstract:
The Covid-19 pandemic has resulted in 550 million cases and 6.3 million fatalities, with the virus severely affecting the lungs and cardiovascular system. A study utilizes a VGG16 model adapted for a 12-Lead ECG Image database to assess the disease’s impact on cardiovascular health. The research addresses the challenge of data imbalance by experimenting with different training approaches: using balanced datasets, imbalanced datasets, and class weight adjustments for imbalanced datasets. These models are designed for a three-class multiclass classification of ECG images: Abnormal, Covid-19, and Normal categories. Performance evaluations, including accuracy scores, confusion matrices, and classification reports, show promising results. The model trained on a balanced dataset achieved a 90% accuracy rate. When trained on an imbalanced dataset, the accuracy dropped to 82%. However, with class weight adjustments, the accuracy rebounded to 87%. The study proves that the adapted VGG16 model
can effectively handle both balanced and imbalanced datasets. Further testing and enhancements can be carried out using additional datasets, making it a valuable tool for understanding the cardiovascular implications of Covid-19.
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