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 a more 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.

中文翻译：

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一种结合 Vision Transformers 和 Graph Convolutional Networks 的新型混合模型，用于猴痘疾病的有效诊断


由于当前诊断技术的局限性，猴痘的准确诊断具有挑战性，这些技术难以解释皮肤病变复杂的视觉和结构特征。本研究旨在开发一种新的混合模型，将 Vision Transformers （ViT）、ResNet50 和 AlexNet 的优势与图卷积网络 （GCN） 相结合，以提高猴痘诊断的准确性。我们的方法捕捉了皮肤病变内的视觉特征和结构关系，提供了一种更全面的分类方法。对两个不同数据集的严格测试表明，ViT+GCN 模型实现了卓越的准确性，尤其擅长 100% 的二元分类和 97% 的准确率的多类分类。这些发现表明，整合视觉和结构信息可以提高诊断可靠性。虽然前景广阔，但此模型需要进一步开发，包括更大的数据集和实时应用程序的优化。总体而言，这种方法推进了皮肤病学诊断，并在诊断其他皮肤相关疾病方面具有更广泛的应用潜力。