{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T18:13:56Z","timestamp":1788459236511,"version":"build-2803163510"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The emergence of monkeypox as a global health concern highlights the need for innovative detection methods that improve upon polymerase chain reaction, which is costly, time-consuming, and poses risks of contagion to healthcare personnel.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study proposed a lightweight deep learning framework to enhance monkeypox lesion detection using skin image data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data augmentation and a novel edge enhancement algorithm are applied, employing contrast-limited adaptive histogram equalization and bilateral filters to refine skin images. The framework is tested across six pretrained deep learning models and one novel hybrid deep model, DenseNet121\u2009+\u2009ConvNeXt-Tiny (DN-CXT). Performance is evaluated using accuracy, F1-score, and precision, with optimization through Adam, root mean square propagation, and stochastic gradient descent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed DN-CXT model achieved the highest performance, with a test accuracy of 97%, F1-score of 97%, and precision of 99%. Applied techniques such as DenseNet121, MobileNetV2, InceptionV3, and ConvNeXt-Tiny also showed exceptional results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed framework significantly advances medical image detection for monkeypox lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications<\/jats:title>\n                    <jats:p>These findings support the integration of artificial intelligence-driven methodologies into monkeypox detection workflows, potentially improving diagnostic efficiency, reducing risks to medical personnel, and enhancing healthcare response to emerging infectious diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261415651","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:30:02Z","timestamp":1771950602000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Detection of monkeypox skin lesions using edge enhancement algorithms integrated with hybrid deep learning"],"prefix":"10.1177","volume":"12","author":[{"given":"Hoor ul","family":"Ain","sequence":"first","affiliation":[{"name":"Government Sadiq College Women University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shabana","family":"Ramzan","sequence":"additional","affiliation":[{"name":"Government Sadiq College Women University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Munwar Iqbal","sequence":"additional","affiliation":[{"name":"University of Engineering and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5429-9835","authenticated-orcid":false,"given":"Ali","family":"Raza","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farwa","family":"Raoof","sequence":"additional","affiliation":[{"name":"University of Lahore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1133-3965","authenticated-orcid":false,"given":"Norma","family":"Latif Fitriyani","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5640-4413","authenticated-orcid":false,"given":"Muhammad","family":"Syafrudin","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-5208","authenticated-orcid":false,"given":"Seung","family":"Won Lee","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine"},{"name":"Department of Metabiohealth, Sungkyunkwan University, Suwon, Republic of Korea"},{"name":"Personalized Cancer Immunotherapy Research Center, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea"},{"name":"Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea"},{"name":"Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/cit703"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmaid.2022.102459"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/v12111257"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1157670"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-024-10121-7"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-023-08408-4"},{"key":"e_1_3_2_8_2","first-page":"637","article-title":"Deep learning models for the detection of monkeypox skin lesion on digital skin images","volume":"14","author":"Alrusaini OA","year":"2023","unstructured":"Alrusaini OA. Deep learning models for the detection of monkeypox skin lesion on digital skin images. Int J Adv Comput Sci Appl 2023; 14: 637\u2013644.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Kilic I Yaman O. A statistical feature extractor-based method using row and column grid for monkeypox detection. In: 2024 8th international artificial intelligence and data processing symposium (IDAP) Malatya Turkiye 21\u201322 Sept. 2024 pp.1\u20135. Piscataway NJ: IEEE.","DOI":"10.1109\/IDAP64064.2024.10711003"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107109"},{"key":"e_1_3_2_11_2","article-title":"Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review","volume":"4","author":"Jones O","year":"2022","unstructured":"Jones O, Matin R, Van der Schaar M, et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health 2022; 4: e466\u2013e476.","journal-title":"Lancet Digit Health"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"G\u00fcrb\u00fcz S Aydin G. Monkeypox skin lesion detection using deep learning models. In: 2022 international conference on computers and artificial intelligence technologies (CAIT) Quzhou China 4\u20136 Nov. 2022 pp.66\u201370. Piscataway NJ: IEEE.","DOI":"10.1109\/CAIT56099.2022.10072140"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Magboo VPC Magboo MSA. Comparative analysis of optimization algorithms applied to monkeypox classification. In: 2023 IEEE 64th international scientific conference on information technology and management science of Riga Technical University (ITMS) Riga Latvia 5\u20136 Oct. 2023 pp.1\u20136. 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