A novel multi-scale context aggregation and feature pooling network for Mpox classification

Mpox, previously known as monkeypox, poses a growing global health threat due to its rising incidence. Rapid and accurate identification of Mpox lesions is crucial, especially in resource-limited settings where traditional diagnostics face delays and demand specialized resources. This study introduces a deep learning model that leverages MobileNetV2, a Multi-Scale Context Aggregator (MSCA), and a Feature Pooling Block to improve Mpox detection using medical images. The MSCA module employs dilated convolutions and global pooling to capture multi-scale features, while the Feature Pooling Block enhances spatial and channel dependencies, achieving refined feature representation. This architecture maintains computational efficiency, making it suitable for deployment in low-resource settings. Evaluated on four diverse datasets, the model achieved high performance: MSLDV1 recorded 93.62% accuracy and 94.28% precision; MSLDV2 reached 100% accuracy and precision; MSID reported 96.15% accuracy and 96.13% precision; and the self-collected dataset achieved 98.80% accuracy and precision. These results underscore the model's superior accuracy and generalization, positioning it as a promising solution for Mpox classification in clinical and research applications. © 2025 Elsevier Ltd