Hybrid Vae-XGBoost Framework for Efficient Classification of Diabetic Foot Ulcer Images

N. Nagarani, Gokul Priyan G. V., Sivanesan R., Sukha Dev A.

2025

Abstract

Diabetic Foot Ulcer (DFU) classification is critical for early classify and planning for manage, with a view to minimizing complications. In this article, a new hybrid model is developed, with Variational Autoencoder (VAE) for feature extraction and XGBoost for classify and with a view to improving accuracy and efficiency in classify of DFU images. VAE learns a low-dimensional and discriminative feature representation of ulcer images, encoding significant structures and textures and dimensionality reduction. Features extracted via VAE are then fed into an optimized XGBoost classify, with a view to improving decision-making via gradient-boosted trees. The proposed model is compared with a benchmarked DFU dataset and contrasted with traditional deep networks, with considerable performance improvement in accuracy, precision, recall, and F1-score. Experimental observations confirm that combining VAE for unsupervised feature extraction with XGBoost for classify enormously improves robustness and generalizability. This hybrid model introduces an efficient and interpretable model for computerized DFU classify, with a view to supporting clinicians in early and correct classify.

Paper Citation

in Harvard Style

Nagarani N., V. G., R. S. and A. S. (2025). Hybrid Vae-XGBoost Framework for Efficient Classification of Diabetic Foot Ulcer Images. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 209-214. DOI: 10.5220/0013895500004919

in Bibtex Style

@conference{icrdicct`2525,
author={N. Nagarani and Gokul V. and Sivanesan R. and Sukha A.},
title={Hybrid Vae-XGBoost Framework for Efficient Classification of Diabetic Foot Ulcer Images},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={209-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013895500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Hybrid Vae-XGBoost Framework for Efficient Classification of Diabetic Foot Ulcer Images
SN - 978-989-758-777-1
AU - Nagarani N.
AU - V. G.
AU - R. S.
AU - A. S.
PY - 2025
SP - 209
EP - 214
DO - 10.5220/0013895500004919
PB - SciTePress