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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

Figures & Tables

Fig. 1.

Workflow of the proposed model.
Workflow of the proposed model.

Fig. 2.

The proposed model.
The proposed model.

Fig. 3.

Segmentation result (epicardial and mediastinal fats).
Segmentation result (epicardial and mediastinal fats).

Fig. 4.

Training and validation loss of the proposed model over epochs.
Training and validation loss of the proposed model over epochs.

Fig. 5.

Performance analysis.
Performance analysis.

Performance analysis_

ModelFOA-ID3 [%]FOA-NB [%]FOA-XGBoost [%]
Accuracy929597
Specificity919596
Precision909497
Recall949498
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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