<![CDATA[Monkeypox diagnosis can initially be conducted through expert physical examination based on characteristic lesions. However, laboratory confirmation using PCR is still essential, these tests are often hampered by limitations such as high costs, lengthy processing times, and a general lack of detailed symptom knowledge among patients. In light of these issues, image-based diagnostic methods offer a more efficient solution, given that monkeypox manifests as visible lesions on the skin that can be accurately detected using a deep learning. This study employs Transformer network-based deep learning for classifying skin diseases. To improve model robustness and mitigate the limitations of the relatively small dataset, we designed a comprehensive data augmentation pipeline that incorporates both positional and color transformations, including rotation, horizontal and vertical flipping, zooming, shearing, and brightness, contrast, hue, and saturation adjustments. Furthermore, a k-fold cross-validation strategy was employed, where the entire dataset was partitioned into k equal-sized folds to ensure a reliable and unbiased evaluation of the model performance. The Swin Transformer leverages advanced transformer network to analyze images, emphasizing hierarchical relationships within images. Swin Transformer enhances the convolutional Transformer architecture by substituting the standard multi-head-self-attention (MSA) mechanism with a shifted window-based MSA module It enhances efficiency over traditional transformer models by incorporating a shifted window mechanism, which reduces computational demands. The average global accuracy achieved was 0.99 (99%), which is further supported by the AUC values obtained for each disease category. The model achieved an AUC of 1.00 for chickenpox, cowpox, and hand-foot-mouth disease (HFMD), indicating excellent discriminative capability for these classes. Meanwhile, the remaining classes, including healthy skin, measles, and monkeypox, achieved AUC values of 0.99 and 0.98, respectively. These results demonstrate that the proposed Hierarchical Swin Transformer model provides highly reliable classification performance across all skin disease categories included in the dataset.]]>
