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Monkeypox Skin Lesion Detection with Deep Learning and Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 23 |
| Year of Publication: 2023 |
| Authors: Saznila Islam, Fhamida Akter Nishi, Tahmina Akter, Muhammad Anwarul Azim |
10.5120/ijca2023922984
|
Saznila Islam, Fhamida Akter Nishi, Tahmina Akter, Muhammad Anwarul Azim . Monkeypox Skin Lesion Detection with Deep Learning and Machine Learning. International Journal of Computer Applications. 185, 23 ( Jul 2023), 39-45. DOI=10.5120/ijca2023922984
Abstract
New outbreak diseases are taken under the great consideration of public health due to the frightful experience of COVID-19 in 2020. That is why Monkeypox disease manifestation in 2022 created awareness of all health-conscious. As before the outbreak of Monkeypox disease was known as African regional disease health professionals were lack of information about it. There had been about 79,151 confirmed cases in over 111 countries as of November. Monkeypox disease symptoms are closely resemble to other skin diseases like chicken pox, smallpox skin rashes which made the diagnosis challenging. Polymerase chain reaction (PCR), molecular biology protocols a rare tool to detect monkeypox disease. That’s why Computer-based detection models will be helpful, affordable where Polymerase chain reaction in unavailable or expensive. With machine learning and deep learning many diseases even like COVID-19 have been successfully detected. Machine learning and deep learning approach have been performed to classify skin image normal, monkey pox, other class. Different pre-trained models of CNN along with CNN, ML and ensemble technique performed. Among all VGG19 come up with the highest accuracy, 99.52%. With VGG16 accuracy was 98.56%. Applying ResNet-50, DenseNet-121, InceptionV3, MobileNetV2, CNN hyper parameter accuracy reached about 86.06%, 90.86%, 99.04% and 99.04%,98.55% respectively.
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