Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

The study is an investigation testing the accuracy of deep learning models in the detection of Monkeypox. The disease is relatively new and difficult for physicians to detect. Data for the skins were obtained from Google via web-scraping with Python’s BeautifulSoup, SERP API, and requests libraries. The images underwent scrutiny by professional physicians to determine their validity and classification. The researcher extracted the images’ features using two CNN models - GoogLeNet and ResNet50. Feature selection from the images involved conducting principal component analysis. Classification employed Support Vector Machines, ResNet50, VGG-16, SqueezeNet, and InceptionV3 models. The results showed that all the models performed relatively the same. However, the most effective model was VGG-16 (accuracy = 0.96, F1-score = 0.92). It is an affirmation of the usefulness of artificial intelligence in the detection of the Monkeypox disease. Subject to the approval of national health authorities, the technology can be used to help detect the disease faster and more conveniently. If integrated into a mobile application, it can be members of the public to self-diagnose before seeking official diagnoses from approved hospitals. The researcher recommends further research into the models and building bigger image databases that will power more reliable analyses.

@article{Alrusaini2023,
  title     = {Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {1},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Othman A. Alrusaini},
  doi       = {10.14569/IJACSA.2023.0140170},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140170}
}