Deep Learning Based Cardiovascular Disease Detection Using ECG Images
Deep Learning Based Cardiovascular Disease Detection Using ECG Images |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-10 |
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| Year of Publication : 2025 | ||
| Author : S.G. Bagul, S.B. Bagal, S.V. Chaudhari, B.S. Agarkar | ||
| DOI : 10.14445/22315381/IJETT-V73I10P124 | ||
How to Cite?
S.G. Bagul, S.B. Bagal, S.V. Chaudhari, B.S. Agarkar,"Deep Learning Based Cardiovascular Disease Detection Using ECG Images", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.312-326, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P124
Abstract
Cardiovascular disease is the most significant cause of mortality throughout the globe, as stated by the World Health Organization (WHO). Researchers are interested in detecting Cardiovascular Disease (CVD) using electrocardiography (ECG) images due to their simplicity of use, explainability, visualization, and representational potential. Based on the Deep Learning-Based Two-Way Feature Depiction (DLTWFD), which is derived from a 2-D Deep Convolutional Neural Network (2D DCNN) and a 1-D Deep Convolutional Neural Network (1D-DCNN), this research presents a CVD detection method based on the DLTWFD. For the purpose of providing connection and correlation in the initial ECG pattern, the 2-D DCNN takes the raw ECG pictures as input. To give the correlation between various texture and shape properties of the ECG pictures and to describe the local and global changes in the ECG pattern that are caused by CVD, the 1-D DCNN accepts the Modified Local Ternary Pattern (MLTP) and the Histogram of Oriented Gradient (HOG) features. By combining the deep qualities from raw ECG pictures, texture, and form features of the ECG, the DLTWFD presented improves the uniqueness of the features. The proposed DLTWFD achieves an overall accuracy of 98.20%, a recall of 98.20%, a precision of 98.10%, and an F1-score of 98.05%, surpassing the current state of the art.
Keywords
Biomedical Image Processing, Cardiovascular Disease Detection, Deep Convolution Neural Network, Histogram of Oriented Gradients, Local Ternary pattern, Texture Descriptor, Machine Learning.
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