Deep Convolutional Network Modeling for ECG Image Analysis in Cardiovascular Disease Detection

Cardiovascular diseases (CVDs) remain the foremost cause of global mortality, accounting for nearly one-third of deaths worldwide. Early detection of cardiac abnormalities is essential to reduce mortality and ensure timely treatment. Electrocardiograms (ECGs) are among the most widely used diagnostic tools for monitoring cardiac activity. However, manual interpretation of ECGs is error-prone and highly dependent on medical expertise. This paper presents a Convolutional Neural Network (CNN)-based framework for automated ECG image classification. The dataset, consisting of 928 ECG images across four categories— Normal, Abnormal, History of Myocardial Infarction (HMI), and Myocardial Infarction (MI) was preprocessed through grayscale conversion, noise reduction, cropping, resizing, and normalization. A custom CNN architecture was trained on this dataset, achieving a classification accuracy of 97.92% on test data, with strong precision, recall, and F1-scores across all categories. The system was deployed using a Flask-based web application that provides real-time predictions and visualizations. The proposed solution demonstrates the applicability of deep learning in medical diagnostics, offering a reliable and scalable approach for CVD detection.

@article{ijter2025212509080724,
  author = {Pilla Divya and Dr. M. V. V. Siva Prasad},
  title = {Deep Convolutional Network Modeling for ECG Image Analysis in Cardiovascular Disease Detection},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {5},
  pages = {13-22},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212509080724/deep-convolutional-network-modeling-for-ecg-image-analysis-in-cardiovascular-disease-detection},
  abstract = {Cardiovascular diseases (CVDs) remain the foremost cause of global mortality, accounting for
  nearly one-third of deaths worldwide. Early detection of cardiac abnormalities is essential to reduce mortality
  and ensure timely treatment. Electrocardiograms (ECGs) are among the most widely used diagnostic tools for
  monitoring cardiac activity. However, manual interpretation of ECGs is error-prone and highly dependent on
  medical expertise. This paper presents a Convolutional Neural Network (CNN)-based framework for
  automated ECG image classification. The dataset, consisting of 928 ECG images across four categories—
  Normal, Abnormal, History of Myocardial Infarction (HMI), and Myocardial Infarction (MI) was
  preprocessed through grayscale conversion, noise reduction, cropping, resizing, and normalization. A custom
  CNN architecture was trained on this dataset, achieving a classification accuracy of 97.92% on test data, with
  strong precision, recall, and F1-scores across all categories. The system was deployed using a Flask-based
  web application that provides real-time predictions and visualizations. The proposed solution demonstrates
  the applicability of deep learning in medical diagnostics, offering a reliable and scalable approach for CVD
  detection.},
  keywords = {Electrocardiogram (ECG), Convolutional Neural Network (CNN), Cardiovascular Disease Detection, Deep Learning, Medical Image Classification, Flask Web Application},
  month = {Sep},
}

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