Enhanced Monkeypox with Image Processing Technology Utilizing Deep Learning for Classification

Monkeypox is a disease that can spread from animals to humans. It is caused by the monkeypox virus (MPXV) and can make people very sick. It is a globally prevalent infectious disease with the potential to cause outbreaks. This study aimed to develop a reliable method for identifying individuals who have contracted monkeypox that can be utilized even before hospitalization. To achieve this, we used a publicly available dataset from Kaggle as the training set and developed a deep learning model that classifies diseases into two categories: monkeypox and non-monkeypox. We fine-tuned a pre-trained convolutional neural network (CNN) model, ResNet-50, using our dataset and achieved an accuracy of 92%, a precision of 93%, a recall of 93%, and an F1-score of 93%. To evaluate the efficiency of our model, we compared its performance with that of a highly skilled doctor in terms of precision, recall, and F1-score. Our findings suggest that the developed model is a simple, cost-effective, and easy-to-understand alternative technique for screening for monkeypox prior to admission for the general population and healthcare providers. This innovative technology could contribute significantly to the global efforts to control and eliminate monkeypox outbreaks.