{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:45:59Z","timestamp":1792622759677,"version":"4.5.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["Science Progress"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Monkeypox (mpox) is a zoonotic infectious disease caused by the mpox virus and characterized by painful body lesions, fever, headaches, and exhaustion. Since the report of the first human case of mpox in Africa, there have been multiple outbreaks, even in nonendemic regions of the world. The emergence and re-emergence of mpox highlight the critical need for early detection, which has spurred research into applying deep learning to improve diagnostic capabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This research aims to develop a robust hybrid long short-term memory (LSTM)-convolutional neural network (CNN) model with a Convolutional Block Attention Module (CBAM) to provide a potential tool for the early detection of mpox.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A hybrid LSTM-CNN multi-stream deep learning model with CBAM was developed and trained using the Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0). We employed LSTM layers for preliminary feature extraction, CNN layers for further feature extraction, and CBAM for feature conditioning. The model was evaluated with standard metrics, and gradient-weighted class activation maps (Grad-CAM) and local interpretable model-agnostic explanations (LIME) were used for interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved an F1-score, recall, and precision of 94%, an area under the curve of 95.04%, and an accuracy of 94%, demonstrating competitive performance compared to the state-of-the-art models. This robust performance highlights the reliability of our model. LIME and Grad-CAM offered insights into the model's decision-making process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The hybrid LSTM-CNN multi-stream deep learning model with CBAM successfully detects mpox, providing a promising early detection tool that can be integrated into web and mobile platforms for convenient and widespread use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00368504251331706","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T05:55:01Z","timestamp":1743227701000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A hybrid long short-term memory-convolutional neural network multi-stream deep learning model with Convolutional Block Attention Module incorporated for monkeypox detection"],"prefix":"10.1177","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4925-7547","authenticated-orcid":false,"given":"Benjamin Appiah","family":"Yeboah","sequence":"first","affiliation":[{"name":"Kwame Nkrumah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kojo Sam","family":"Micah","sequence":"additional","affiliation":[{"name":"Kwame Nkrumah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2539-7361","authenticated-orcid":false,"given":"Isaac","family":"Acquah","sequence":"additional","affiliation":[{"name":"Kwame Nkrumah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kofi Ampomah","family":"Mensah","sequence":"additional","affiliation":[{"name":"Kwame Nkrumah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"100424","article-title":"Phasing out monkeypox: mpox is the new name for an old disease","volume":"17","author":"Damaso CR","year":"2023","unstructured":"Damaso CR. Phasing out monkeypox: mpox is the new name for an old disease. Lancet Reg Health - Am 2023; 17: 100424.","journal-title":"Lancet Reg Health - Am"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0168664"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/v12111257"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1699-0463.1959.tb00328.x"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0007791"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0010141"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3201\/eid2502.181222"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.4269\/ajtmh.2005.73.428"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2018.00241"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3201\/eid2406.180017"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/v14102155"},{"key":"e_1_3_3_13_2","unstructured":"WHO Director-General declares mpox outbreak a public health emergency of international concern https:\/\/www.who.int\/news\/item\/14-08-2024-who-director-general-declares-mpox-outbreak-a-public-health-emergency-of-international-concern (accessed 5 September 2024)."},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214229"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.12513"},{"key":"e_1_3_3_16_2","article-title":"Mpox","author":"Mathieu E","year":"2022","unstructured":"Mathieu E, Spooner F, Dattani S, et al.Mpox. Our world in data, https:\/\/ourworldindata.org\/explorers\/monkeypox (2022, accessed 26 January 2024).","journal-title":"Our world in data"},{"key":"e_1_3_3_17_2","unstructured":"World Health Organisation. WHO declares end of mpox emergency calls for sustained efforts for long-term management of the disease - PAHO\/WHO | Pan American Health Organization. World Health Organisation https:\/\/www.paho.org\/en\/news\/11-5-2023-who-declares-end-mpox-emergency-calls-sustained-efforts-long-term-management-disease (2023 accessed 22 August 2024)."},{"key":"e_1_3_3_18_2","unstructured":"2022-24 Mpox (Monkeypox) Outbreak: Global Trends. World Health Organization https:\/\/worldhealthorg.shinyapps.io\/mpx_global\/ (2024 accessed 26 June 2024)."},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmaid.2022.102459"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0281815"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3253868"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41545-z"},{"key":"e_1_3_3_23_2","doi-asserted-by":"crossref","unstructured":"Haque ME Ahmed MR Nila RS et al. Classification of human monkeypox disease using deep learning models and attention mechanisms. 2022. Epub ahead of print 21 November 2022. 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