Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms.

Monkeypox (Mpox), caused by the monkeypox virus, has become a global concern due to its rising cases and resemblance to other rash-causing diseases like chickenpox and measles. Traditional diagnostic methods, including visual examination and PCR tests, face limitations such as misdiagnoses, high costs, and unavailability in resource-limited areas. Existing deep learning-based approaches, while effective, often rely on centralized datasets, raising privacy concerns and scalability issues. To address these challenges, this study proposes ResViT-FLBoost model, a federated learning-based framework integrating ResNet and Vision Transformer (ViT) architectures with ensemble classifiers, XGBoost and LightGBM. This system ensures decentralized training across healthcare facilities, preserving data privacy while improving classification performance. The Monkeypox Skin Lesion Dataset (MSLD), consisting of 3192 augmented images, is utilized for training and testing. The framework, implemented in Python, leverages federated learning to collaboratively train models without data sharing, and adaptive attention mechanisms to focus on critical lesion features. Results demonstrate a detection accuracy of 98.78%, significantly outperforming traditional and existing methods in terms of precision, recall, and robustness. The new framework of ResViT-FLBoost incorporates ResNet convolutional features together with ViT contextual representations which are boosted by dynamic attention components. The system employs a deep learning pipeline integration that serves under a federated learning arcitecture that protects patient privacy because it lets distributed model training proceed from various hospital hubs without moving sensitive health information to one central server. The ensemble classifiers XGBoost and LightGBM improve diagnostic outcomes by merging local as well as global features within their classification decisions. These technical innovations provide strong diagnostic ability together with privacy-safe implementation capabilities for deployment in actual healthcare infrastructure. This framework provides a scalable, privacy-preserving solution for Mpox detection, particularly suitable for deployment in resource-constrained settings.