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Journal : Journal of Applied Data Sciences

Monkeypox Disease Classification Based on Skin Images Using Hierarchical Swin Transformer-Based Convolutional Neural Network Approach Ayu, Putu Desiana Wulaning; Sukket, Sasiwimol; Sutikno, Sutikno; Prihatini, Putu Manik; Pradipta, Gede Angga; Hostiadi, Dandy Pramana
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1313

Abstract

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.
Co-Authors Amry wicaksono, Amry Anggreni Antarajaya, I Nyoman Suraja Artamerta, Aditya Naray Candra Ahmadi, Candra Chawaphan, Pharan Danang Setyo Utomo, Danang Setyo Dian Pramana S.Kom., M.Kom, Dian Erma Sulistyo Rini Erma Sulistyo Rini, Erma Sulistyo Eva Hariyanti Evi Triandini Fatonah, Nenden Siti Firgiawan Faira Florentina Tatrin Kurniati Gede Angga Pradipta Gede Angga Pradipta, Gede Angga Gede, Angga Pradipta Hendra Wijaya Hilmi, Muhammad Riza I G K G Puritan Wijaya. ADH, I G K G I Gede Edy Artana I Gede Harsemadi I Gede Ngurah Widya Pradnyana, I Gede Ngurah Widya I Gede Putu Krisna Juliharta I GKG Puritan Wijaya, I GKG I Gusti Ayu Dewi Suardi, I Gusti Ayu Dewi I Gusti Ngurah Darma Paramartha I Gusti Nym Adi Purnama Putra, I Gusti Nym I Made Darma Susila I Made Darma Susila, I Made I Made Darma Susila, I Made Darma I Made Liandana I Nyoman Triwantara Putra, I Nyoman I Putu Harry Wibawa Eka Putra, I Putu Harry Wibawa I Putu Oka Aditya Pratama I Putu Ramayasa, I Putu I Putu Widiantara, I Putu I Wayan Eka Mahardika, I Wayan Eka I Wayan Nesa Masjaya Perdana, I Wayan Nesa I.B. Putra Utama Dhiatmika, I.B. Putra Utama Ida Bagus Suradarma Indah, Hene Nor Intaran, Arya Ngurah Irene Realyta Halldy Trosi Tangkawarow Kadek Evanna Sidarta, Kadek Evanna Komang Yuli Santika Made Liandana Made Liandana, Made Made Sudarma Made, Liandana Mohammad Yazdi Pusadan Muhammad Riza Hilmi Ni Ketut Dewi Ari Jayanti Ni Luh Putri Srinadi Nurfalah, Rizal Farhan Nabila Nuriansyah, Devin Garmenta Pande Wira Andika, Pande Perimawati, Ni Nyoman Eny Pradita, Agus Hendra Putu Desiana Wulaning Ayu Putu Manik Prihatini Rizky Adhitya Ridholloh, Rizky Adhitya Rosalia Hadi Roy Rudolf Huizen Rustamaji, Abdullah Saputra, Made Wisnu Adhi Shofwan Hanief Sudawati, Luh Dwi Ari Sukket, Sasiwimol Sutikno Sutikno Tubagus Mahendra Kusuma Widhyastuti, Luh Putu Wiwien Wulaning Ayu, Putu Desiana Wulaning Ayu, Putu Desiana Yohanes Priyo Atmojo Yohanes Priyo Atmojo Yohanes Priyo Atmojo, Yohanes Yohanes Priyo Atmojo, Yohanes Priyo Yudhi Pratiwindhya, Yudhi