{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:53:55Z","timestamp":1791899635554,"version":"4.3.4"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, King Khalid University, Kingdom of Saudi Arabia","award":["RGP.1\/382\/43"],"award-info":[{"award-number":["RGP.1\/382\/43"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijerph20054422","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T03:06:43Z","timestamp":1677726403000},"page":"4422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":145,"title":["Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques"],"prefix":"10.3390","volume":"20","author":[{"given":"Ameera S.","family":"Jaradat","sequence":"first","affiliation":[{"name":"Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2523-7819","authenticated-orcid":false,"given":"Rabia Emhamed","family":"Al Mamlook","sequence":"additional","affiliation":[{"name":"Department Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI 49008, USA"},{"name":"Department of Aeronautical Engineering, Al Zawiya University (Seventh of April University), Al Zawiya City P.O. Box 16418, Libya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9461-5935","authenticated-orcid":false,"given":"Naif","family":"Almakayeel","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1900-420X","authenticated-orcid":false,"given":"Nawaf","family":"Alharbe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Applied College, Taibah University, Madinah 46537, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5359-1519","authenticated-orcid":false,"given":"Ali Saeed","family":"Almuflih","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3221-2773","authenticated-orcid":false,"given":"Ahmad","family":"Nasayreh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Gharaibeh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Gharaibeh","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine, Hashemite University, Zarqa 13133, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Gharaibeh","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanin","family":"Bzizi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science, Western Michigan University, Kalamazoo, MI 49008, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"ref_1","unstructured":"Hussain, M.A., Islam, T., Chowdhury , F.U.H., and Islam, B.R. (2022). Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?. bioRxiv."},{"key":"ref_2","unstructured":"Moore, M.J., Rathish, B., and Zahra, F. (2022). StatPearls, StatPearls Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1056\/NEJMoa2207323","article-title":"Monkeypox Virus Infection in Humans across 16 Countries\u2014April\u2013June 2022","volume":"387","author":"Thornhill","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_4","unstructured":"(2023, January 23). Monkeypox. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/monkeypox."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.virs.2022.07.006","article-title":"Monkeypox virus: A re-emergent threat to humans","volume":"37","author":"Gong","year":"2022","journal-title":"Virol. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/21681163.2020.1824685","article-title":"A CNN-based methodology for breast cancer diagnosis using thermal images","volume":"9","author":"Benaggoune","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_7","unstructured":"Ali, S.N., Ahmed, M., Paul, J., Jahan, T., Sani, S.M., Noor, N., and Hasan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10916-022-01863-7","article-title":"Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application","volume":"46","author":"Sahin","year":"2022","journal-title":"J. Med. Syst."},{"key":"ref_9","unstructured":"Ahsan, M.M., Uddin, M., Farjana, M., Sakib, A., Al Momin, K., and Luna, S.A. (2022). Image Data Collection and Implementation of Deep Learning-Based Model in Detecting Monkeypox Disease Using Modified VGG16. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s10916-022-01868-2","article-title":"Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches","volume":"46","author":"Sitaula","year":"2022","journal-title":"J. Med. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kumar, V. (2022). Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkepox from digital skin images. medRxiv.","DOI":"10.1101\/2022.09.11.22278797"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yasmin, F., Hassan, M., Zaman, S., Aung, S., Karim, A., and Azam, S. (2022). A Forecasting Prognosis of the Monkeypox Outbreak Based on a Comprehensive Statistical and Regression Analysis. Computation, 10.","DOI":"10.3390\/computation10100177"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abdelhamid, A.A., El-Kenawy, E.-S.M., Khodadadi, N., Mirjalili, S., Khafaga, D.S., Alharbi, A.H., Ibrahim, A., Eid, M.M., and Saber, M. (2022). Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm. Mathematics, 10.","DOI":"10.3390\/math10193614"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Altun, M., G\u00fcr\u00fcler, H., \u00d6zkaraca, O., Khan, F., and Khan, J. (2023). Monkeypox Detection Using CNN with Transfer Learning. Sensors, 23.","DOI":"10.3390\/s23041783"},{"key":"ref_15","unstructured":"Mohbey, K.K., Meena, G., Kumar, S., and Lokesh, K. (2022). A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_17","unstructured":"(2023, January 23). Data_Monkeypox|Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/ahmadnasayrah\/data-monkeypox."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","unstructured":"Tan, M., and Le, Q.V. (2019, January 10\u201315). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/5\/4422\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:45:26Z","timestamp":1760121926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/5\/4422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["ijerph20054422"],"URL":"https:\/\/doi.org\/10.3390\/ijerph20054422","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}