Maheskumar V, Vijayarajeswari R
Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India.
Department of Information Technology, Sona College of Technology, Salem, Tamil Nadu, India.
Sci Rep. 2025 Jul 9;15(1):24728. doi: 10.1038/s41598-025-05391-5.
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.
猴痘由猴痘病毒引起，由于其病例数不断增加且与水痘和麻疹等其他引起皮疹的疾病相似，已成为全球关注的问题。包括目视检查和聚合酶链反应（PCR）检测在内的传统诊断方法存在局限性，如误诊、成本高以及在资源有限地区无法使用等。现有的基于深度学习的方法虽然有效，但往往依赖集中式数据集，引发了隐私问题和可扩展性问题。为应对这些挑战，本研究提出了ResViT-FLBoost模型，这是一个基于联邦学习的框架，将残差网络（ResNet）和视觉Transformer（ViT）架构与集成分类器XGBoost和LightGBM相结合。该系统确保在医疗设施之间进行分散式训练，在保护数据隐私的同时提高分类性能。由3192张增强图像组成的猴痘皮肤病变数据集（MSLD）用于训练和测试。该框架用Python实现，利用联邦学习在不共享数据的情况下协同训练模型，并采用自适应注意力机制聚焦于关键病变特征。结果表明检测准确率为98.78%，在精度、召回率和鲁棒性方面显著优于传统方法和现有方法。ResViT-FLBoost的新框架将ResNet卷积特征与ViT上下文表示相结合，并由动态注意力组件进行增强。该系统采用深度学习管道集成，在联邦学习架构下运行，保护患者隐私，因为它允许在各个医院中心进行分布式模型训练，而无需将敏感健康信息转移到一个中央服务器。集成分类器XGBoost和LightGBM通过在分类决策中合并局部和全局特征来改善诊断结果。这些技术创新为在实际医疗基础设施中部署提供了强大的诊断能力和隐私安全实施能力。该框架为猴痘检测提供了一个可扩展的、保护隐私的解决方案，特别适合在资源受限的环境中部署。
