{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T12:57:30Z","timestamp":1792587450833,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In this research, we demonstrate a Deep Convolutional Neural Network-based classification model for the detection of monkeypox. Monkeypox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles in symptoms. The early diagnosis of monkeypox helps doctors cure it more quickly. Therefore, pre-trained models are frequently used in the diagnosis of monkeypox, because the manual analysis of a large number of images is labor-intensive and prone to inaccuracy. Therefore, finding the monkeypox virus requires an automated process. The large layer count of convolutional neural network (CNN) architectures enables them to successfully conceptualize the features on their own, thereby contributing to better performance in image classification. The scientific community has recently articulated significant attention in employing artificial intelligence (AI) to diagnose monkeypox from digital skin images due primarily to AI\u2019s success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal). The majority of images in our research are collected from publicly available datasets. This study suggests an adaptive k-means clustering image segmentation technique that delivers precise segmentation results with straightforward operation. Our preliminary computational findings reveal that the proposed model could accurately detect patients with monkeypox. The best overall accuracy achieved by ResNet101 is 94.25%, with an AUC of 98.59%. Additionally, we describe the categorization of our model utilizing feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which provides a more in-depth understanding of particular properties that distinguish the monkeypox virus.<\/jats:p>","DOI":"10.3390\/diagnostics13091639","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T01:32:53Z","timestamp":1683509573000},"page":"1639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"M.","family":"Lakshmi","sequence":"first","affiliation":[{"name":"Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9945-5969","authenticated-orcid":false,"given":"Raja","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1093\/cid\/cit703","article-title":"Human monkeypox","volume":"58","author":"McCollum","year":"2014","journal-title":"Clin. Infect. Dis."},{"key":"ref_2","unstructured":"World Health Organisation (2022). Multi-Country Monkeypox, World Health Organisation."},{"key":"ref_3","first-page":"e28146","article-title":"Getting Ahead of Monkeypox: Learning from the COVID-19 pandemic experience to prevent the potentially new monkeypox pandemic","volume":"95","author":"Ly","year":"2022","journal-title":"J. Med. Virol."},{"key":"ref_4","unstructured":"WHO (2022). Monkeypox Fact Sheet, WHO."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Bunge, E.M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L.R., and Steffen, R. (2022). The changing epidemiology of human monkeypox\u2014A potential threat? A systematic review. PLoS Negl. Trop. Dis., 16.","DOI":"10.1371\/journal.pntd.0010141"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s40265-022-01742-y","article-title":"Prevention and Treatment of Monkeypox","volume":"82","author":"Rizk","year":"2022","journal-title":"Drugs"},{"key":"ref_8","unstructured":"(2023, February 05). World Health Organization-Monkeypox Fact Sheet. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/monkeypox."},{"key":"ref_9","unstructured":"Diagnostic Tests (2023, February 05). Rapid Diagnostic Testing for Response to the Monkeypox Outbreak, Available online: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9290387\/."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Gupta, K.D., Islam, M.M., Sen, S., Rahman, M.L., and Shakhawat Hossain, M. (2020). Covid-19 symptoms detection based on nasnetmobile with explainable ai using various imaging modalities. Mach. Learn. Knowl. Extr., 2.","DOI":"10.3390\/make2040027"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Nazim, R., Siddique, Z., and Huebner, P. (2021). Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare, 9.","DOI":"10.3390\/healthcare9091099"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1148\/ryai.210217","article-title":"Performance of a chest radiograph ai diagnostic tool for covid-19: A prospective observational study","volume":"4","author":"Sun","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101763","DOI":"10.1016\/j.jocs.2022.101763","article-title":"Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images","volume":"63","author":"Akbarimajd","year":"2022","journal-title":"J. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104704","DOI":"10.1016\/j.compbiomed.2021.104704","article-title":"Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images","volume":"136","author":"Momeny","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103795","DOI":"10.1016\/j.compbiomed.2020.103795","article-title":"Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks","volume":"121","author":"Ardakani","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Roy, K., Chaudhuri, S.S., Ghosh, S., Dutta, S.K., Chakraborty, P., and Skin, R.S. (2019, January 18\u201320). Disease detection based on different Segmentation Techniques. Proceedings of the 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India.","DOI":"10.1109\/OPTRONIX.2019.8862403"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep learning for health informatics","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1001\/jamainternmed.2018.7117","article-title":"Deep learning in medicine\u2014Promise, progress, and challenges","volume":"179","author":"Wang","year":"2019","journal-title":"JAMA Intern. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1371\/journal.pone.0217293","article-title":"Classification of skin lesions using tv ransfer learning and augmentation with Alex-net","volume":"14","author":"Hosny","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_21","unstructured":"Bradley, P., and Fayyad, U. (1998, January 24\u201327). Refining initial points for k-means clustering. Proceedings of the International Conference on Machine Learning, Madison, WI, USA."},{"key":"ref_22","first-page":"1","article-title":"Automated Classification of Whole-Body SPECT Bone Scan Images with VGG-Based Deep Networks","volume":"20","author":"Lin","year":"2023","journal-title":"Int. Arab. J. Inf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104534","DOI":"10.1016\/j.chemolab.2022.104534","article-title":"Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia","volume":"224","author":"Showkat","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1002\/ima.22812","article-title":"A deep learning approach for classification of COVID and pneumonia using DenseNet-201","volume":"33","author":"Sanghvi","year":"2023","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14","DOI":"10.12688\/f1000research.122288.1","article-title":"Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB\u2014Retrained AlexNet convolutional neural network","volume":"12","year":"2023","journal-title":"F1000Research"},{"key":"ref_26","first-page":"1151034","article-title":"An approach for recognizing COVID-19 cases using convolutional neural networks applied to CT scan images","volume":"11510","author":"Cuong","year":"2020","journal-title":"Proc. SPIE"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vrban\u010di\u010d, G., Pe\u010dnik, \u0160., and Podgorelec, V. (2020, January 24\u201326). Identification of COVID-19 X-ray images using CNN with optimized tuning of transfer learning. Proceedings of the 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Novi Sad, Serbia.","DOI":"10.1109\/INISTA49547.2020.9194615"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1007\/s10489-020-01943-6","article-title":"Deep neural network to detect COVID-19: One architecture for both CT Scans and Chest X-rays","volume":"51","author":"Mukherjee","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29051","DOI":"10.1007\/s11042-021-10907-y","article-title":"Convolutional neural networks for the classification of chest X-rays in the IoT era","volume":"80","author":"Almezhghwi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108885","DOI":"10.1016\/j.ress.2022.108885","article-title":"Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis","volume":"229","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/JAS.2022.105935","article-title":"Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks","volume":"10","author":"Li","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_32","unstructured":"Ali, S.N., Ahmed, T., Paul, J., and Jahan, T. (2022). Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. arXiv."},{"key":"ref_33","unstructured":"Ahsan, M., Uddin, M.R., Farjana, M., Sakib, A.N., 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_34","doi-asserted-by":"crossref","unstructured":"Islam, T., Hussain, M.A., Uddin, F., Chowdhury, H., and Islam, B.M.R. Can artificial intelligence detect Monkeypox from digital skin images?. bioRxiv, 2022.","DOI":"10.1101\/2022.08.08.503193"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-019-0439-2","article-title":"Application research of convolution neural network in image classification of icing monitoring in power grid","volume":"2019","author":"Lu","year":"2019","journal-title":"Eurasip J. Image Video Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"79","DOI":"10.2174\/157016461302160514000940","article-title":"Protein folds prediction with hierarchical structured SVM","volume":"13","author":"Li","year":"2016","journal-title":"Curr. Proteom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11280-015-0381-x","article-title":"Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier","volume":"20","author":"Manek","year":"2017","journal-title":"World Wide Web"},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u2018Why should I trust you?\u2019 Explaining the predictions of any classifier. Proceedings of the KDD \u201816: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA,.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_42","unstructured":"Cian, D., van Gemert, J., and Lengyel, A. (2020). Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task. arXiv."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/9\/1639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:04Z","timestamp":1760124604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/9\/1639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13091639"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13091639","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}