Efficient Prediction and Analysis of Monkeypox Skin Lesion: A Comparative Study for Web based Application

After the coronavirus pandemic, another deadly disease, known as monkeypox, began to spread. This was so alarming that the World Health Organization declared it a global public health emergency. As of 31st January 2023, there have been 85449 cases of monkeypox reported in 110 countries worldwide. It is difficult to diagnose because its symptoms resemble those of chicken pox, small-pox, etc. The clinical diagnosis of monkeypox is performed using the Polymerase chain reaction (PCR) test, which takes a considerable amount of time to determine the result. Any non-clinical test that could aid in identifying monkeypox in suspected patients would be advantageous. Various deep learning models were found to be useful for this purpose, provided sufficient training data is available. We’ve used already existing datasets and extended them to have a sufficiently large dataset. This dataset is then fed to pre-trained deep-learning models, including ResNet50, EfficientNetB3, InceptionV3, and MobileNetV2. It has aided in comparing the accuracy of these models. Based on our analysis, we have employed best performing model i.e., EfficientNetB3 in a web application with a user-friendly interface to interact with it.