Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence

Tushar Nayak, Krishnaraj Chadaga, Niranjana Sampathila, Hilda Mayrose, G. Muralidhar Bairy, Srikanth Prabhu, Swathi S. Katta, Shashikiran Umakanth

Research output: Contribution to journalArticlepeer-review


Monkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many symptoms, the disease also causes skin rashes, and medical imaging can be used to diagnose the virus successfully. However, other diseases such as smallpox, chickenpox, and measles also cause similar skin rashes. Hence, artificial intelligence (AI) and machine learning (ML) can be highly beneficial in diagnosing Mpox from other similar diseases. After extensive model training, it is advantageous to use a standard camera to capture skin images of an infected patient and run it against deep learning (DL) models. In this research, we have used transfer learning models such as residual networks and SqueezeNet to diagnose Mpox from measles, chickenpox and healthy patients. An average accuracy of 91.19% and an F1-score of 92.55% were obtained for the Mpox class. The findings show that the models can be useful in detecting the contagious virus. Since the classifiers are easily deployable, they can be used on camera-ready devices such as phones and laptops.

Original languageEnglish
Article number2225698
JournalApplied Mathematics in Science and Engineering
Issue number1
Publication statusPublished - 2023

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Engineering(all)
  • Applied Mathematics


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