AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH026270075
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Mohammad Reza Faisal
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ORIGINAL RESEARCH ARTICLE

Improving mpox identification under imbalanced data using geometric transformation and color augmentation with graph neural networks

Mohammad Reza Faisal1* ,  Luu Duc Ngo2 ,  Radityo Adi Nugroho1 ,  Fatma Indriani1 ,  Muhammad Rafi1
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1 Department of Computer Science, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University, Banjarbaru, South Borneo , Indonesia
2 Department of Computer Science, Faculty of Engineering and Technology, Bac Lieu University, Bac Lieu, Ca Mau , Vietnam
AIH, 026270075 https://doi.org/10.36922/AIH026270075
Received: 29 June 2026 | Revised: 10 August 2026 | Accepted: 14 August 2026 | Published online: 8 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Mpox lesion identification from skin images remains challenging because lesion appearances often overlap with other dermatological diseases and available data are commonly imbalanced. This study proposes a graph-based multi-class classification framework that combines class-balancing augmentation, convolutional neural network (CNN)-based feature extraction, and graph neural classification. Experiments were conducted on the Mpox Skin Lesion Dataset Version 2.0 using three data configurations: (i) the original imbalanced data, (ii) a balanced version generated through geometric transformation augmentation, and (iii) a balanced version generated through combined geometric transformation and color-space augmentation. Image features were extracted using eight CNN backbones (EfficientNetB4, AlexNet, VGG16, ResNet50, DenseNet121, GoogleNetV3/InceptionV3, MobileNetV2, and LeNet), then transformed into graphs by selecting k-nearest-neighbor candidates and retaining edges according to cosine-similarity filtering, and finally classified using graph convolutional networks (GCN) and graph attention networks (GAT). Across 48 model combinations, data balancing improved macro-level performance compared with the original imbalanced setting. The best overall result was achieved by GCN with VGG16 on the geometrically and color-augmented balanced data, reaching 92.00% accuracy, 91.55% macro F1-score, 98.81% macro area under the receiver operating characteristic curve (one-versus-rest), 93.54% macro precision, and 90.32% macro recall. The best GAT result was obtained by MobileNetV2 on the geometrically augmented balanced data, with 90.67% accuracy, 89.25% macro F1-score, 98.48% macro area under the receiver operating characteristic curve (one-versus-rest), 89.28% macro precision, and 90.27% macro recall. Although GCN showed a more consistent empirical performance trend than GAT, the difference was not statistically significant. These findings indicate that integrating balanced augmentation, CNN-derived representations, and graph learning is a promising strategy for mpox lesion identification within the scope of the present experiments; however, broader external validation and explainability-oriented analysis are still required before real-world clinical deployment.

Graphical abstract
Keywords
Mpox
Image classification
Imbalanced data
Data augmentation
Graph convolutional networks
Graph attention networks
Funding
This research was financially supported by the Lambung Mangkurat University Research Grant for 2025, with grant number 1842/UN8.2/PG/2025.
Conflict of interest
The authors declare that they have no conflict of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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