CNN Based Detection of Cardiovascular Diseases from ECG Images
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912008361738240 |
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| author | Sayin, Irem Gursoy, Rana Cicek, Buse Mert, Yunus Emre Ozturk, Fatih Pamukcu, Taha Emre Sevimli, Ceylin Deniz Uvet, Huseyin |
| author_facet | Sayin, Irem Gursoy, Rana Cicek, Buse Mert, Yunus Emre Ozturk, Fatih Pamukcu, Taha Emre Sevimli, Ceylin Deniz Uvet, Huseyin |
| contents | This study develops a Convolutional Neural Network (CNN) model for detecting myocardial infarction (MI) from Electrocardiogram (ECG) images. The model, built using the InceptionV3 architecture and optimized through transfer learning, was trained using ECG data obtained from the Ch. Pervaiz Elahi Institute of Cardiology in Pakistan. The dataset includes ECG images representing four different cardiac conditions: myocardial infarction, abnormal heartbeat, history of myocardial infarction, and normal heart activity. The developed model successfully detects MI and other cardiovascular conditions with an accuracy of 93.27%. This study demonstrates that deep learning-based models can provide significant support to clinicians in the early detection and prevention of heart attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CNN Based Detection of Cardiovascular Diseases from ECG Images Sayin, Irem Gursoy, Rana Cicek, Buse Mert, Yunus Emre Ozturk, Fatih Pamukcu, Taha Emre Sevimli, Ceylin Deniz Uvet, Huseyin Medical Physics Machine Learning Image and Video Processing This study develops a Convolutional Neural Network (CNN) model for detecting myocardial infarction (MI) from Electrocardiogram (ECG) images. The model, built using the InceptionV3 architecture and optimized through transfer learning, was trained using ECG data obtained from the Ch. Pervaiz Elahi Institute of Cardiology in Pakistan. The dataset includes ECG images representing four different cardiac conditions: myocardial infarction, abnormal heartbeat, history of myocardial infarction, and normal heart activity. The developed model successfully detects MI and other cardiovascular conditions with an accuracy of 93.27%. This study demonstrates that deep learning-based models can provide significant support to clinicians in the early detection and prevention of heart attacks. |
| title | CNN Based Detection of Cardiovascular Diseases from ECG Images |
| topic | Medical Physics Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2408.16800 |