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Volume 20
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10.3390/ijerph20054422
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Review

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

by
Ameera S. Jaradat
1,
Rabia Emhamed Al Mamlook
2,3,*,
Naif Almakayeel
4,
Nawaf Alharbe
5,
Ali Saeed Almuflih
4,
Ahmad Nasayreh
1,
Hasan Gharaibeh
1,
Mohammad Gharaibeh
6,
Ali Gharaibeh
7 and
Hanin Bzizi
8
1
Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan
2
Department Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI 49008, USA
3
Department of Aeronautical Engineering, Al Zawiya University (Seventh of April University), Al Zawiya City P.O. Box 16418, Libya
4
Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia
5
Department of Computer Science, Applied College, Taibah University, Madinah 46537, Saudi Arabia
6
Department of Medicine, Faculty of Medicine, Hashemite University, Zarqa 13133, Jordan
7
Department of Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
8
Department of Biomedical Science, Western Michigan University, Kalamazoo, MI 49008, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(5), 4422; https://doi.org/10.3390/ijerph20054422
Submission received: 5 February 2023 / Revised: 26 February 2023 / Accepted: 27 February 2023 / Published: 1 March 2023
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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 mpox; image analysis; deep learning; classification; image processing; early diagnosis

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MDPI and ACS Style

Jaradat, A.S.; Al Mamlook, R.E.; Almakayeel, N.; Alharbe, N.; Almuflih, A.S.; Nasayreh, A.; Gharaibeh, H.; Gharaibeh, M.; Gharaibeh, A.; Bzizi, H. Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. Int. J. Environ. Res. Public Health 2023, 20, 4422. https://doi.org/10.3390/ijerph20054422

AMA Style

Jaradat AS, Al Mamlook RE, Almakayeel N, Alharbe N, Almuflih AS, Nasayreh A, Gharaibeh H, Gharaibeh M, Gharaibeh A, Bzizi H. Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. International Journal of Environmental Research and Public Health. 2023; 20(5):4422. https://doi.org/10.3390/ijerph20054422

Chicago/Turabian Style

Jaradat, Ameera S., Rabia Emhamed Al Mamlook, Naif Almakayeel, Nawaf Alharbe, Ali Saeed Almuflih, Ahmad Nasayreh, Hasan Gharaibeh, Mohammad Gharaibeh, Ali Gharaibeh, and Hanin Bzizi. 2023. "Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques" International Journal of Environmental Research and Public Health 20, no. 5: 4422. https://doi.org/10.3390/ijerph20054422

APA Style

Jaradat, A. S., Al Mamlook, R. E., Almakayeel, N., Alharbe, N., Almuflih, A. S., Nasayreh, A., Gharaibeh, H., Gharaibeh, M., Gharaibeh, A., & Bzizi, H. (2023). Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. International Journal of Environmental Research and Public Health, 20(5), 4422. https://doi.org/10.3390/ijerph20054422

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