Mpox is a re-emerging zoonotic viral disease that attracted the attention of the whole world because of its spreading transmission and clinical similarity with other skin diseases. It is highly important that this identification is fast and accurate, even in remotely located areas or resource-limited settings. However, the conventional centralized deep learning models exhibit severe limitations regarding data privacy, modality variation, and scalability across varied clinical environments. To this end, this paper presents MetaFusion-FL, a new federated meta-learning framework that combines cross-modality image analysis based on a hybrid Transformer-Capsule model with Hierarchical Attention-Based Multimodal Fusion (HAMFM). The model can work on multi-source images as input, namely smartphone images, dermoscopic images, and clinical images, which are processed locally at edge hospitals without raw data transmission. Reptile federated meta-learning strategy guarantees quick personalization of models and global generalization. When evaluated on a wide dataset, MetaFusion-FL has a higher classification accuracy of 99.46%, precision of 99.52%, recall of 99.40%, and F1-score of 99.46% compared to other current models, including ViT-RLXGBFL (99.12%) and ResViT-FLBoost (98.78%). The framework is also resistant to image noise and is consistent and stable across federated clients. Besides, SHAP and Grad-CAM++ explanations are used to ensure interpretability in a clinical context. MetaFusion-FL is therefore a leap in the development of AI-based, privacy-preserving, and generalizable skin disease classification, particularly Mpox.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Kalphana K R, Maheskumar V, Vijayarajeswari R and Sasikala K;
Methodology: Kalphana K R and Maheskumar V;
Software: Vijayarajeswari R and Sasikala K;
Data Curation: Kalphana K R and Maheskumar V;
Writing- Original Draft Preparation: Kalphana K R, Maheskumar V, Vijayarajeswari R and Sasikala K;
Visualization: Vijayarajeswari R and Sasikala K;
Investigation: Kalphana K R and Maheskumar V;
Supervision: Vijayarajeswari R and Sasikala K;
Validation: Kalphana K R and Maheskumar V;
Writing- Reviewing and Editing: Kalphana K R, Maheskumar V, Vijayarajeswari R and Sasikala K; All authors reviewed the results and approved the final version of the manuscript.
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Kalphana K R
Department of Agricultural Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India.
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Cite this article
Kalphana K R, Maheskumar V, Vijayarajeswari R and Sasikala K, “MetaFusion-FL: A Cross Modality Federated Meta Learning Framework for Robust and Explainable Healthcare System”, Journal of Machine and Computing, vol.5, no.3, pp. 1787-1802, July 2025, doi: 10.53759/7669/jmc202505141.
