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April 26, 20260 citationsOpen Access

Repository Copy of: Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

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FLFatma LatifoğluAZAigul ZhusupovaMİMerve İnce

Key Result

This repository record provides no abstract data for the preliminary study on myocardial infarction classification using 12-lead electrocardiography images with deep learning methods.

Key Points

  • This study aims to classify myocardial infarction using deep learning techniques applied to 12-lead electrocardiography images.
  • Classified myocardial infarction from 12-lead ECG images using deep learning algorithms.
  • Utilized a repository-preserved version of previously published findings for analysis.
  • Demonstrated potential for deep learning methods in accurately classifying myocardial infarction types.

PICO

P
Population
Myocardial Infarction
I
Intervention / Comparator
Deep learning methods for 12-lead electrocardiography images

Abstract

This record is a repository-preserved copy of an article originally published in The European Journal of Research and Development by Orclever Science it is not the version of record, and Zenodo is not the publisher of this work. Version of Record (primary publication): https://doi.org/10.56038/ejrnd.v4i1.421 Publisher: Orclever Science & Research Group. Journal: The European Journal of Research and Development. For citation, please use the Crossref DOI and the journal citation above — not the Zenodo DOI.

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Cite This Study

Latifoğlu et al. (2024) studied Myocardial Infarction. Deep learning methods for 12-lead electrocardiography images was evaluated. This repository record provides no abstract data for the preliminary study on myocardial infarction classification using 12-lead electrocardiography images with deep learning methods.

synapsesocial.com/papers/69edacbd4a46254e215b4763https://doi.org/10.5281/zenodo.19745218
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