Abstract:
According to World Health Organization (WHO) statistics, monkeypox has been identified as an epidemic in 127 nations so far, and it is spreading quickly over the globe. While the rashes and skin lesions associated with monkeypox usually mimic those of other poxes, including chickenpox and measles. Due to these similarities, it could be challenging for medical professionals to identify monkeypox based just on the appearance of lesions and rashes. Because monkeypox was uncommon before in the current outbreak, healthcare professionals lack knowledge in this area. But the scientific community has demonstrated a rising interest in implementing Artificial Intelligence in Monkeypox prediction and detection from digital skin images as a result of the success of image processing approaches in COVID-19 detection. In this study, we have applied three cutting-edge deep learning models which are InceptionV3, MobileNetV3, and DenseNet201, referred to as transfer learning models, to detect monkeypox on skin images using the publicly available Monkeypox Skin Image Dataset 2022 with four classes. According to our research, transfer learning models can detect monkeypox with a top 93.59% accuracy for the InceptionNet-V3 pre-trained model from three implemented algorithms on digitized skin images. For further research, larger training images are required to train those deep learning models to achieve a higher vigorous detection rate.
