Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques

Ameera S. Jaradat, Rabia Emhamed Al Mamlook (), Naif Almakayeel, Nawaf Alharbe, Ali Saeed Almuflih, Ahmad Nasayreh, Hasan Gharaibeh, Mohammad Gharaibeh, Ali Gharaibeh and Hanin Bzizi
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Ameera S. Jaradat: Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan
Rabia Emhamed Al Mamlook: Department Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI 49008, USA
Naif Almakayeel: Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia
Nawaf Alharbe: Department of Computer Science, Applied College, Taibah University, Madinah 46537, Saudi Arabia
Ali Saeed Almuflih: Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia
Ahmad Nasayreh: Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan
Hasan Gharaibeh: Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan
Mohammad Gharaibeh: Department of Medicine, Faculty of Medicine, Hashemite University, Zarqa 13133, Jordan
Ali Gharaibeh: Department of Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
Hanin Bzizi: Department of Biomedical Science, Western Michigan University, Kalamazoo, MI 49008, USA

IJERPH, 2023, vol. 20, issue 5, 1-20

Abstract: The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.

Keywords: mpox; image analysis; deep learning; classification; image processing; early diagnosis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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