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An Enhanced Measurement of Epicardial Fat Segmentation and Severity Classification using Modified U-Net and FOA-guided XGBoost Cover

An Enhanced Measurement of Epicardial Fat Segmentation and Severity Classification using Modified U-Net and FOA-guided XGBoost

Measurement Science Review
Volume 25 (2025): Issue 2 (April 2025)
By: K Rajalakshmi and  S Palanivel Rajan  
Open Access
|Jun 2025

Authors

K Rajalakshmi

k.rajalakshmiphd@gmail.com

Department of Artificial Intelligence and Data Science, Kamaraj College of Engineering and Technology, India

S Palanivel Rajan

Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, India
DOI: https://doi.org/10.2478/msr-2025-0012 | Journal eISSN: 1335-8871
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Language: English
Page range: 93 - 99
Submitted on: Jun 23, 2024
|
Accepted on: Apr 15, 2025
|
Published on: Jun 7, 2025
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
epicardial fat segmentation,
U-Net architecture,
falcon optimization algorithm,
feature selection,
severity classification
Related subjects:
Engineering,
Electrical engineering,
Control engineering, metrology and testing

© 2025 K Rajalakshmi, S Palanivel Rajan, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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