Diagnosing MonkeyPox from Skin Indications using ArtificialIntelligence Method

Following the first reported case to World Health Organization (WHO) on 7 May 2022, an ongoing outbreak of monkeypox, a viral zoonosis, has faced public health with a new challenge globally that has shown public health importance on a global scale. In this study, we aim to leverage the promise of Artificial Intelligence (AI) models to diagnose monkeypox disease using images of skin manifestations. An open-access dataset containing skin images of monkeypox, chickenpox, measles, and normal patients was used in this study. After preprocessing and image augmentation, an overall number of 1754 images has been chosen (80% for tarin and 20% for test). Then a modified pre-trained DenseNet-121 model was utilized for the four-class classification of mentioned diseases. Achieved results show that the modified pre-trained DenseNet201 model offers an average accuracy of 99.04%, precision of 97.85%, sensitivity of 97.66%, specificity of 99.21%, and F1-Score of 97.61%. Our proposed model offers promising performance for differentiating monkeypox infected patients from chickenpox, measles, and normal patients. Our proposed model can be used in hospitals and clinics to help physicians accurately diagnose monkeypox disease.

کلیدواژه های Diagnosing MonkeyPox from Skin Indications using ArtificialIntelligence Method:

Monkeypox ، Artificial Intelligence ، Skin Indications