Image-Enhanced Heart Disease Risk Assessment using CNN Algorithm

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ID: 312951
2024
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Abstract
The study delves into the profound dual nature of the heart, as both a vital organ sustaining human life and a powerful symbol deeply interwoven in human culture and emotions. It acknowledges the pervasive global challenge of heart disease, encompassing a myriad of heart and blood vessel disorders with severe health implications. The study's research methodology emphasizes the crucial selection of effective techniques for identifying and categorizing electrocardiogram (ECG) data, outlining research objectives, chosen algorithms, dataset description, and the proposed workflow. In this research, an analysis of the Cardiovascular ECG Images dataset is conducted, with a focus on data preprocessing steps, including image resizing, grayscale conversion, and dataset division for training and testing.
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imported_1777058018_69ebc0e2f23c3 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aneesa Malik
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/532
Keywords Keywords not found

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