Deep Learning for ECG Image Analysis: A Lightweight Approach for Covid-19 Diagnosis

被引：0
|
作者：
El-Naggar, Ahmed Hatem [1 ]
Fawzi, Sahar Ali [1 ]
Tantawi, Manal [2 ]
机构：
[1] Nile Univ, Informat Technol & Comp Sci, Giza, Egypt
[2] Ain Shams Univ, Fac Comp & Informat Sci, Cairo, Egypt
来源：
2024 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND SMART INNOVATION, ICMISI 2024 | 2024年
关键词：
COVID-19; ECG; CNN; Gamma Correction;
D O I：
10.1109/ICMISI61517.2024.10580506
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Since late 2019, Covid-19 has broken out causing immense pressure on healthcare systems worldwide. Fast detection of Covid-19 has become crucial in controlling and slow-pacing the virus outbreak. Innovative methods that are cheap, fast, and accurate for Covid-19 detection are of high importance to aid in the efforts of containment of the disease. In this study a novel method is proposed for Covid-19 detection through analysis of ECG image records. Three models are introduced for three classification schemas, Normal vs Covid-19, Covid-19 vs non Covid-19, Normal vs Covid-19 vs Abnormal HeartBeat. An overall accuracy of 98.6%, 99%, and 90% respectively is achieved. Automatic detection of Covid-19 with computer aided systems using ECG images is achievable and very promising.
引用
收藏
页码：186 / 189
页数：4
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