A novel hybrid model combining Vision Transformers and Graph Convolutional Networks for monkeypox disease effective diagnosis

Abstract

Accurate diagnosis of monkeypox is challenging due to the limitations of current diagnostic techniques, which struggle to account for skin lesions' complex visual and structural characteristics. This study aims to develop a novel hybrid model that combines the strengths of Vision Transformers (ViT), ResNet50, and AlexNet with Graph Convolutional Networks (GCN) to improve monkeypox diagnostic accuracy. Our method captures both the visual features and structural relationships within skin lesions, offering amore comprehensive approach to classification. Rigorous testing on two distinct datasets demonstrated that the ViT+GCN model achieved superior accuracy, particularly excelling in binary classification with 100% accuracy and multi-class classification with a 97% accuracy rate. These findings indicate that integrating visual and structural information enhances diagnostic reliability. While promising, this model requires further development, including larger datasets and optimization for real-time applications. Overall, this approach advances dermatological diagnostics and holds potential for broader applications in diagnosing other skin-related diseases.