{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T23:40:12Z","timestamp":1780098012398,"version":"3.54.0"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["APMIS"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The initial step in diagnosing tuberculosis involves the microscopic examination of sputum samples using acid\u2010fast staining to identify bacilli. However, this conventional method is labor\u2010intensive, requires specialized expertise, is susceptible to errors, and has limited sensitivity. Research literature indicates that deep learning models demonstrate significant potential for detecting acid\u2010fast bacilli (AFB) in sputum smear preparations. This study investigates the effectiveness of deep learning methods in identifying AFB within sputum smear samples. Our objective was to assess the performance of these models in tuberculosis diagnosis based on microscopic examination and to identify improvements they could bring in terms especially of sensitivity and availability within this field. We employed several transfer learning models: DenseNet201, ResNet101V2, Xception, InceptionResNetV2, and InceptionV3. In order to determine the effectiveness of these models, basic performance metrics such as accuracy, recall, precision, and F1 score were used. Among the transfer learning models we recommended, the InceptionV3 and Xception models exhibited the highest performance, achieving 99.00% high performance across all evaluation metrics. Our findings underscore that deep learning models can be effectively utilized for rapid and accurate detection of\n                    <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                    in acid\u2010fast stained sputum preparations.\n                  <\/jats:p>","DOI":"10.1111\/apm.70138","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T03:56:51Z","timestamp":1767758211000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detection of\n                    <i>Mycobacterium tuberculosis<\/i>\n                    in Ziehl\u2013Neelsen Stained Sputum Smear Specimens Using Deep Learning Techniques"],"prefix":"10.1111","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7222-6506","authenticated-orcid":false,"given":"Ozlem","family":"Genc","sequence":"first","affiliation":[{"name":"Department of Medical Microbiology, Faculty of Medicine Kutahya Health Science University  Kutahya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5399-3374","authenticated-orcid":false,"given":"Serpil","family":"Genc","sequence":"additional","affiliation":[{"name":"Department of Medical Microbiology, Faculty of Medicine Kutahya Health Science University  Kutahya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-5888","authenticated-orcid":false,"given":"Cuneyt","family":"Ozdemir","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering Siirt University  Siirt T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1548-0444","authenticated-orcid":false,"given":"Mehmet Ali","family":"Gedik","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine Kutahya Health Science University  Kutahya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"World Health Organization (WHO) \u201cGlobal Tuberculosis Report 2024 \u201dhttps:\/\/iris.who.int\/bitstream\/handle\/10665\/379339\/9789240101531\u2010eng.pdf?sequence=1."},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11195826"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcp\/aqaa215"},{"issue":"11","key":"e_1_2_7_5_1","first-page":"120","article-title":"The OpenCV Library","volume":"25","author":"Bradski G.","year":"2000","journal-title":"Dr. Dobb's Journal of Software Tools"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1411.1792"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.18280\/ts.400216"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"G.Huang Z.Liu L.Van Der Maaten andK. Q.Weinberger \u201cDensely Connected Convolutional Networks \u201d2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu HI USA 2017 pp. 2261\u20132269 https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_2_7_11_1","doi-asserted-by":"crossref","unstructured":"F.Chollet \u201cXception: Deep Learning With Depthwise Separable Convolutions \u201d2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu HI USA 2017 pp. 1800\u20131807 https:\/\/doi.org\/10.1109\/CVPR.2017.195.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_7_12_1","doi-asserted-by":"crossref","unstructured":"C.Szegedy S.Ioffe V.Vanhoucke andA.Alemi \u201cInception\u2010v4 Inception\u2010ResNet and the Impact of Residual Connections on Learning \u201dIn Proceedings of the Thirty\u2010First AAAI Conference on Artificial Intelligence (AAAI) 4278\u20134284 2017 https:\/\/doi.org\/10.48550\/arXiv.1602.07261.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.18280\/ts.400219"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2024.200365"},{"key":"e_1_2_7_15_1","doi-asserted-by":"crossref","unstructured":"M.Hu Y.Liu Y.Zhang T.Guan andY.He \u201cAutomatic Detection of Tuberculosis Bacilli in Sputum Smear Scans Based on Subgraph Classification \u201d2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE) Shenzhen China 2019 pp. 1\u20137 https:\/\/doi.org\/10.1109\/ICMIPE47306.2019.9098210.","DOI":"10.1109\/ICMIPE47306.2019.9098210"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49342-4_6"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010018\u20103564\u20104"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.28919\/cmbn\/5035"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979\u2010022\u201001133\u2010w"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13132230"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci\u20102024101101"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021210013"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22218497"}],"container-title":["APMIS"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/apm.70138","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/apm.70138","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/apm.70138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:59:25Z","timestamp":1772963965000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/apm.70138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1111\/apm.70138"],"URL":"https:\/\/doi.org\/10.1111\/apm.70138","archive":["Portico"],"relation":{},"ISSN":["0903-4641","1600-0463"],"issn-type":[{"value":"0903-4641","type":"print"},{"value":"1600-0463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-05-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70138"}}