A Supervised Learning-Based Classification Technique for Precise Identification of Monkeypox Using Skin Imaging

The monkeypox epidemic has spread to nearly every nation. Governments implement several strict policies, to stop the virus that causes monkeypox. For effective handling and treatment, early identification and diagnosis of monkeypox using digital skin lesion images is critical, and this work employed deep learning architectures to achieve this goal. This article presents a supervised learning-based classification method designed for the precise identification of monkeypox cases. The analysis was conducted using an open-source dataset from Kaggle, consisting of digital images of monkeypox, which were processed using advanced image processing and deep learning techniques. The data was categorized based on findings related and unrelated to monkeypox. A deep neural network with 50 layers and up to 35 folds was utilized to identify regions of interest, which could be indicative of characteristics relevant to computer-assisted medical diagnosis and enable us to solve image processing and natural language processing tasks with high accuracy. In terms of performance, the proposed method achieved an accuracy of 96% during cross-validation classification testing. This outcome demonstrates the potential for computer-assisted diagnosis as a supplementary tool for medical professionals. Amid the monkeypox outbreak, this method offers a technical and objective assessment of patients' skin conditions, thereby simplifying the diagnostic process for specialists.

@article{Vandana2025,
  title     = {A Supervised Learning-Based Classification Technique for Precise Identification of Monkeypox Using Skin Imaging},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Vandana and Chetna Sharma and Yonis Gulzar and Mohammad Shuaib Mir},
  doi       = {10.14569/IJACSA.2025.0160262},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160262}
}