Preediction of Cardiovascular Diseases Using ECG Images
- DOI
- 10.2991/978-94-6463-858-5_69How to use a DOI?
- Keywords
- Electrodiagram(ECG); ConvolutionalNueral Network; Myocardinal Infraction(MI)
- Abstract
Cardiovascular disease remains a leading global health concern. Electrocardiograms (ECGs) are widely used for heart disease detection, but manual interpretation is time-consuming and prone to error. This study compares two deep learning models—Convolutional Neural Networks (CNN) and MobileNet—for classifying ECG images into five categories: myocardial infarction, history of MI, abnormal heartbeat, normal, and COVID-19. Using labeled ECG images, both models learn spatial and temporal features. While CNN captures complex patterns well, MobileNet achieves higher accuracy and efficiency due to its lightweight architecture, making it ideal for real-time diagnosis in resource-limited settings. The findings emphasize the role of deep learning in improving cardiac care and the need to balance performance with computational demands.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - S. Ram Prasad Reddy AU - M. Jahnavi AU - K. Samuel AU - P. Mohith AU - V. N. V. S. Abhishek PY - 2025 DA - 2025/11/04 TI - Preediction of Cardiovascular Diseases Using ECG Images BT - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025) PB - Atlantis Press SP - 813 EP - 829 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-858-5_69 DO - 10.2991/978-94-6463-858-5_69 ID - Reddy2025 ER -