Deep Learning Based Monkeypox Virus Detection Model Using Skin Lesion Images

Monkeypox is a very rapidly spreading disease which has raised significant concerns in the public sector as well as to the government of every country, where it is rising in a very high ratio. It has spread to over 40 countries Outside of Africa. The early detection of the monkeypox virus is not too easy because the clues of this virus are the same as chickenpox and measles. However, after Machine Learning came into the era, it made much of the work very easy in the direction of early prediction. Nowadays, researchers are using many deep learning algorithms to get the prediction of monkeypox with a high rate of accuracy. These images are basically trained on the images of monkeypox and also trained to get the difference from other diseases like chickenpox, measles which have similar symptoms. After using these models, we are finding the best accuracy rate prediction on monkeypox prediction. This study involved training using an Inception V3 model on the Monkeypox Images dataset. Which has an accuracy rate of 89 percent. A batch size of 32 with 50 epochs have been used to train the model. The model's performance was assessed through a detailed analysis of its predictions, including an assessment of precision, recall, and accuracy.