Prediction of Monkeypox on the Skin Lesion with the Siamese Deep Learning Model

Abstract

One of the viral diseases that started to cause concern in various parts of the world after the COVID-19 pandemic is the monkeypox virus, which has recently emerged. The virus, which was known in previous years and mostly seen in the Western and Central parts of the African continent, has recently begun to affect different human populations in different ways. Monkeypox is transmitted to humans from an animal infected with the virus or from another human being infected with monkeypox. Among the most basic symptoms are high fever, back and muscle aches, chills, and blisters on the skin. These blisters seen on the skin are sometimes confused with chickenpox and measles, and this causes the diagnosis and, accordingly, the treatment process to be wrong. Therefore, the need for computer-aided systems has increased and the need for more robust and reliable approaches has arisen. In this study, using the deep learning model, the distinction of the blisters seen in the body was made and it was decided whether the disease was monkeypox or another disease (chickenpox and measles). The study consisted of three stages. In the first stage, data were obtained and images of both chickenpox and other diseases were used. In the second stage, the Siamese deep learning model was used, and data were classified. In the last stage, the performance of the classifier was evaluated and accordingly accuracy, precision, recall, F1-score, and confusion matrix were used. At the end of the study, an accuracy score of 91.09% was obtained. This result showed that the developed deep learning-based model can be used in this field.

Keywords

• Artificial intelligence
• Monkeypox
• Siamese deep learning model

References

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