{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:36:22Z","timestamp":1793111782639,"version":"4.5.0"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002889","name":"Cochin University of Science and Technology","doi-asserted-by":"publisher","award":["CUSAT\/PL(UGC) A1\/112\/2021"],"award-info":[{"award-number":["CUSAT\/PL(UGC) A1\/112\/2021"]}],"id":[{"id":"10.13039\/501100002889","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002889","name":"Cochin University of Science and Technology","doi-asserted-by":"publisher","award":["SMNRI project grant"],"award-info":[{"award-number":["SMNRI project grant"]}],"id":[{"id":"10.13039\/501100002889","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microbiological Methods"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.mimet.2026.107718","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T23:15:16Z","timestamp":1790291716000},"page":"107718","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Efficient and accurate tuberculosis diagnosis: Attention Residual U-Net and vision transformer based detection framework"],"prefix":"10.1016","volume":"250","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6110-1831","authenticated-orcid":false,"given":"Greeshma","family":"K.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vishnukumar","family":"S.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.mimet.2026.107718_b1","series-title":"Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation","author":"Alom","year":"2018"},{"key":"10.1016\/j.mimet.2026.107718_b2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11760-014-0708-6","article-title":"Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples","volume":"8","author":"Ayas","year":"2014","journal-title":"Signal Image Video Process."},{"issue":"1","key":"10.1016\/j.mimet.2026.107718_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.mimet.2026.107718_b4","series-title":"2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"382","article-title":"Automatic identification of mycobacterium tuberculosis with conventional light microscopy","author":"Costa","year":"2008"},{"key":"10.1016\/j.mimet.2026.107718_b5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1590\/2446-4740.0524","article-title":"Automatic identification of tuberculosis mycobacterium","volume":"31","author":"Costa Filho","year":"2015","journal-title":"Res. Biomed. Eng."},{"key":"10.1016\/j.mimet.2026.107718_b6","series-title":"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"6263","article-title":"Mycobacterium tuberculosis recognition with conventional microscopy","author":"CostaFilho","year":"2012"},{"key":"10.1016\/j.mimet.2026.107718_b7","doi-asserted-by":"crossref","unstructured":"El-Melegy, M., Mohamed, D., ElMelegy, T., Abdelrahman, M., 2019. Identification of tuberculosis bacilli in ZN-stained sputum smear images: A deep learning approach. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops.","DOI":"10.1109\/CVPRW.2019.00147"},{"key":"10.1016\/j.mimet.2026.107718_b8","series-title":"2021 International Conference on Artificial Intelligence and Computer Science Technology","first-page":"84","article-title":"Detecting and counting tuberculosis bacilli in a microscopic image of patient sputum using gaussian mixture model","author":"Fandriyanto","year":"2021"},{"key":"10.1016\/j.mimet.2026.107718_b9","series-title":"2023 International Conference on Advances in Intelligent Computing and Applications","first-page":"1","article-title":"Identification of tuberculosis bacilli from bright field microscopic sputum smear images using U-Net and random forest classification algorithm","author":"Greeshma","year":"2023"},{"key":"10.1016\/j.mimet.2026.107718_b10","series-title":"2018 IEEE Symposium Series on Computational Intelligence","first-page":"1250","article-title":"Towards automated tuberculosis detection using deep learning","author":"Kant","year":"2018"},{"issue":"4","key":"10.1016\/j.mimet.2026.107718_b11","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/TITB.2009.2028339","article-title":"Classification of mycobacterium tuberculosis in images of ZN-stained sputum smears","volume":"14","author":"Khutlang","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"1","key":"10.1016\/j.mimet.2026.107718_b12","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/j.1365-2818.2009.03308.x","article-title":"Automated detection of tuberculosis in Ziehl-Neelsen-stained sputum smears using two one-class classifiers","volume":"237","author":"Khutlang","year":"2010","journal-title":"J. Microsc."},{"key":"10.1016\/j.mimet.2026.107718_b13","series-title":"Tbimages: A smear microscopy image dataset to support the development of automated bacilli detection in the diagnosis of tuberculosis","author":"Marly G F","year":"2022"},{"issue":"4","key":"10.1016\/j.mimet.2026.107718_b14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/13682199.2023.2167553","article-title":"Enhanced fuzzy gaussian networks for sputum image based mycobacterium detection","volume":"71","author":"Mithra","year":"2023","journal-title":"Imaging Sci. J."},{"key":"10.1016\/j.mimet.2026.107718_b15","series-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"issue":"3","key":"10.1016\/j.mimet.2026.107718_b16","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.bbe.2018.05.007","article-title":"Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods","volume":"38","author":"Panicker","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.mimet.2026.107718_b17","series-title":"Machine Learning for Healthcare Applications","first-page":"343","article-title":"A lightweight convolutional neural network model for tuberculosis bacilli detection from microscopic sputum smear images","author":"Panicker","year":"2021"},{"issue":"4","key":"10.1016\/j.mimet.2026.107718_b18","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s42979-022-01133-w","article-title":"Automatic detection of tuberculosis bacilli from conventional sputum smear microscopic images using densely connected convolutional networks","volume":"3","author":"Panicker","year":"2022","journal-title":"SN Comput. Sci."},{"issue":"2","key":"10.1016\/j.mimet.2026.107718_b19","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.bbe.2014.08.002","article-title":"Separation of overlapping bacilli in microscopic digital tb images","volume":"35","author":"Priya","year":"2015","journal-title":"Biocybern. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.mimet.2026.107718_b20","first-page":"579","article-title":"Image processing techniques for identifying mycobacterium tuberculosis in Ziehl-Neelsen stains","volume":"12","author":"Sadaphal","year":"2008","journal-title":"Int. J. Tuberc. Lung Dis."},{"issue":"2","key":"10.1016\/j.mimet.2026.107718_b21","doi-asserted-by":"crossref","first-page":"027503","DOI":"10.1117\/1.JMI.4.2.027503","article-title":"Ziehl\u2013Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis","volume":"4","author":"Shah","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.mimet.2026.107718_b22","series-title":"2009 International Conference on Digital Image Processing","first-page":"117","article-title":"Detection and quantification of bacilli and clusters present in sputum smear samples: ?a novel algorithm for pulmonary tuberculosis diagnosis","author":"Sotaquira","year":"2009"},{"issue":"9","key":"10.1016\/j.mimet.2026.107718_b23","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/S1473-3099(06)70578-3","article-title":"Fluorescence versus conventional sputum smear microscopy for tuberculosis: A systematic review","volume":"6","author":"Steingart","year":"2006","journal-title":"Lancet Infect. Dis."},{"key":"10.1016\/j.mimet.2026.107718_b24","series-title":"Global tuberculosis report 2022","first-page":"1","author":"W.H. Organization","year":"2022"},{"key":"10.1016\/j.mimet.2026.107718_b25","series-title":"Global tuberculosis report 2023","first-page":"1","author":"W.H. Organization","year":"2023"},{"key":"10.1016\/j.mimet.2026.107718_b26","series-title":"Global tuberculosis report 2024","first-page":"1","author":"W.H. Organization","year":"2024"},{"key":"10.1016\/j.mimet.2026.107718_b27","series-title":"2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies","first-page":"1","article-title":"Automatic classification of light field smear microscopy patches using convolutional neural networks for identifying mycobacterium tuberculosis","author":"Y.P. L\u00f3pez","year":"2017"},{"key":"10.1016\/j.mimet.2026.107718_b28","series-title":"2010 IEEE International Conference on Robotics and Biomimetics","first-page":"41","article-title":"Automatic identification of mycobacterium tuberculosis from ZN-stained sputum smear: Algorithm and system design","author":"Zhai","year":"2010"}],"container-title":["Journal of Microbiological Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167701226003301?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167701226003301?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:01:05Z","timestamp":1793109665000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167701226003301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":28,"alternative-id":["S0167701226003301"],"URL":"https:\/\/doi.org\/10.1016\/j.mimet.2026.107718","relation":{},"ISSN":["0167-7012"],"issn-type":[{"value":"0167-7012","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficient and accurate tuberculosis diagnosis: Attention Residual U-Net and vision transformer based detection framework","name":"articletitle","label":"Article Title"},{"value":"Journal of Microbiological Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mimet.2026.107718","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"107718"}}