Hypergraph convolutional neural networks for clinical diagnosis of monkeypox infections using skin virological images

The Monkeypox virus (Mpoxv), characterized by its distinct vesiculopustular rash, has re-emerged as a significant zoonotic pathogen, posing severe public health risks and potential bioterrorism threats. Although less virulent than smallpox, the persistence of Mpoxv infections necessitates advanced diagnostic tools and proactive mitigation strategies. Dermatological virological imaging is significant for automatic Mpoxv detection and classification, yet its fidelity is often compromised by low-resolution data, mainly in the incipient stages of the infection. Conventional deep learning models mostly struggle to capture higher-order dependencies and complex feature interactions within virological images, leading to suboptimal outcomes. An advanced hybrid hypergraph convolutional neural networks (HGCNs) architecture is introduced in response. In this architecture hypergraph effectively models intricate correlations and enables the detect subtle patterns. At the same time, CNN components contribute robust feature extraction, refined through relational modeling, leads optimal detection and classification of Mpoxv infection. The HGCNs were trained and validated using two different validation approaches, including the Holdout method (HM) and a stratified 3-fold cross-validation (3-FCV), yielding HM accuracy of 0.9888, precision of 0.9813, recall of 0.9958, F1 Score of 0.9885, specificity of 0.9890, Micro AUC of 0.9892, and an average time per epoch of 0.5512 s, while 3-FCV achieved an average accuracy of 0.9917, precision of 0.9931, recall of 0.9912, F1 score of 0.9922, specificity of 0.9941, Micro AUC of 0.9903, and an average time per epoch of 0.6151 s. Furthermore, the use of Grad-CAM facilitates precise localization of infected regions within the images. The performance highlights the proposed model's effectiveness as a powerful tool in computational virology, delivering high accuracy and interpretable diagnostics for Mpoxv infections. Data availability: The dataset is freely available online.