Federated Deep Learning for Monkeypox Disease Detection on GAN-Augmented Dataset

Abstract

After the coronavirus disease 2019 (COVID-19) outbreak, the viral infection known as monkeypox gained significant attention, and the World Health Organization (WHO) classified it as a global public health emergency. Given the similarities between monkeypox and other pox viruses, conventional classification methods encounter difficulties in accurately identifying the disease. Furthermore, sharing sensitive medical data gives rise to concerns about security and privacy. Integrating deep neural networks with federated learning (FL) presents a promising avenue for addressing the challenges of medical data categorization. In light of this, we propose an FL-based framework using deep learning models to classify monkeypox and other pox viruses securely. The proposed framework has three major components: (a) a cycle-consistent generative adversarial network to augment data samples for training; (b) deep learning-based models such as MobileNetV2, Vision Transformer (ViT), and ResNet50 for the classification; and (c) a flower-federated learning environment for security. The experiments are performed using publicly available datasets. In the experiments, the ViT-B32 model yields an impressive accuracy rate of 97.90%, emphasizing the robustness of the proposed framework and its potential for secure and accurate categorization of monkeypox disease.

Keywords

• Cycle GAN
• deep neural network
• federated learning
• WHO
• convolution neural network
• monkeypox detection