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

K Rajalakshmi

K Rajalakshmi

Department of Artificial Intelligence and Data Science, Kamaraj College of Engineering and TechnologyIndia
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Rajalakshmi, K
 und 
S Palanivel Rajan

S Palanivel Rajan

Department of Electronics and Communication Engineering, Velammal College of Engineering and TechnologyMadurai, India
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Palanivel Rajan, S
  
07. Juni 2025
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Online veröffentlicht: 07. Juni 2025
Seitenbereich: 93 - 99
Eingereicht: 23. Juni 2024
Akzeptiert: 15. Apr. 2025
DOI: https://doi.org/10.2478/msr-2025-0012
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© 2025 K Rajalakshmi et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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_

Model FOA-ID3 [%] FOA-NB [%] FOA-XGBoost [%]
Accuracy 92 95 97
Specificity 91 95 96
Precision 90 94 97
Recall 94 94 98
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