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

Vollständiger Abstract

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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.