{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:42Z","timestamp":1740108042329,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07653-z","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T10:02:32Z","timestamp":1659780152000},"page":"21927-21938","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9997-4267","authenticated-orcid":false,"given":"Murat","family":"U\u00e7ar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"7653_CR1","unstructured":"WHO Weekly epidemiological update on COVID-19. https:\/\/www.who.int\/publications\/m\/item\/weekly-epidemiological-update-on-covid-19---30-november-2021. Accessed 30 Nov 2021"},{"key":"7653_CR2","doi-asserted-by":"publisher","first-page":"2000045","DOI":"10.2807\/1560-7917.ES.2020.25.3.2000045","volume":"25","author":"VM Corman","year":"2020","unstructured":"Corman VM, Landt O, Kaiser M et al (2020) Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance 25:2000045","journal-title":"Eurosurveillance"},{"key":"7653_CR3","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1080\/14737159.2020.1757437","volume":"20","author":"A Tahamtan","year":"2020","unstructured":"Tahamtan A, Ardebili A (2020) Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn 20:453\u2013454","journal-title":"Expert Rev Mol Diagn"},{"key":"7653_CR4","doi-asserted-by":"publisher","first-page":"200463","DOI":"10.1148\/radiol.2020200463","volume":"295","author":"A Bernheim","year":"2020","unstructured":"Bernheim A, Mei X, Huang M et al (2020) Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295:200463","journal-title":"Radiology"},{"key":"7653_CR5","doi-asserted-by":"publisher","first-page":"E41","DOI":"10.1148\/radiol.2020200343","volume":"296","author":"X Xie","year":"2020","unstructured":"Xie X, Zhong Z, Zhao W et al (2020) Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology 296:E41\u2013E45","journal-title":"Radiology"},{"key":"7653_CR6","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.jrid.2020.04.001","volume":"7","author":"M Li","year":"2020","unstructured":"Li M (2020) Chest CT features and their role in COVID-19. Radiol Infect Dis 7:51\u201354","journal-title":"Radiol Infect Dis"},{"key":"7653_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2020.120142","volume":"158","author":"J Liu","year":"2020","unstructured":"Liu J, Chang H, Forrest JY-L, Yang B (2020) Influence of artificial intelligence on technological innovation: evidence from the panel data of china\u2019s manufacturing sectors. Technol Forecast Soc Change 158:120142","journal-title":"Technol Forecast Soc Change"},{"key":"7653_CR8","first-page":"1","volume":"7","author":"G Bannerjee","year":"2018","unstructured":"Bannerjee G, Sarkar U, Das S, Ghosh I (2018) Artificial intelligence in agriculture: a literature survey. Int J Sci Res Comput Sci Appl Manag Stud 7:1\u20136","journal-title":"Int J Sci Res Comput Sci Appl Manag Stud"},{"key":"7653_CR9","first-page":"577","volume":"11","author":"D Kaur","year":"2020","unstructured":"Kaur D, Sahdev SL, Sharma D et al (2020) Banking 4.0:\u2018the influence of artificial intelligence on the banking industry & how ai is changing the face of modern day banks.\u2019 Int J Manag 11:577\u2013585","journal-title":"Int J Manag"},{"key":"7653_CR10","doi-asserted-by":"publisher","first-page":"115695","DOI":"10.1016\/j.eswa.2021.115695","volume":"185","author":"M Khan","year":"2021","unstructured":"Khan M, Mehran MT, Haq ZU et al (2021) Applications of artificial intelligence in COVID-19 pandemic: a comprehensive review. Expert Syst Appl 185:115695. https:\/\/doi.org\/10.1016\/j.eswa.2021.115695","journal-title":"Expert Syst Appl"},{"key":"7653_CR11","unstructured":"Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv Prepr https:\/\/arxiv.org\/abs\/2003.09424"},{"key":"7653_CR12","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.30.20047787","author":"LN Mahdy","year":"2020","unstructured":"Mahdy LN, Ezzat KA, Elmousalami HH et al (2020) Automatic x-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. MedRxiv. https:\/\/doi.org\/10.1101\/2020.03.30.20047787","journal-title":"MedRxiv"},{"key":"7653_CR13","doi-asserted-by":"publisher","first-page":"65031","DOI":"10.1088\/1361-6560\/abe838","volume":"66","author":"F Shi","year":"2021","unstructured":"Shi F, Xia L, Shan F et al (2021) Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Phys Med Biol 66:65031. https:\/\/doi.org\/10.1088\/1361-6560\/abe838","journal-title":"Phys Med Biol"},{"key":"7653_CR14","unstructured":"Tang Z, Zhao W, Xie X, et al (2020) Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv Prepr https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.11988"},{"key":"7653_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792","journal-title":"Comput Biol Med"},{"key":"7653_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2020.05.003","author":"Y Pathak","year":"2020","unstructured":"Pathak Y, Shukla PK, Tiwari A et al (2020) Deep transfer learning based classification model for COVID-19 disease. Irbm. https:\/\/doi.org\/10.1016\/j.irbm.2020.05.003","journal-title":"Irbm"},{"key":"7653_CR17","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43:635\u2013640","journal-title":"Phys Eng Sci Med"},{"key":"7653_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan AI, Shah JL, Bhat MM (2020) CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 196:105581","journal-title":"Comput Methods Programs Biomed"},{"key":"7653_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104284","volume":"144","author":"M Heidari","year":"2020","unstructured":"Heidari M, Mirniaharikandehei S, Khuzani AZ et al (2020) Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int J Med Inform 144:104284","journal-title":"Int J Med Inform"},{"key":"7653_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-019-0089-x","volume":"8","author":"NK Chowdhury","year":"2020","unstructured":"Chowdhury NK, Rahman MM, Kabir MA (2020) PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Heal Inf Sci Syst 8:1\u201314","journal-title":"Heal Inf Sci Syst"},{"key":"7653_CR21","doi-asserted-by":"publisher","first-page":"102862","DOI":"10.1016\/j.bspc.2021.102862","volume":"69","author":"E U\u00e7ar","year":"2021","unstructured":"U\u00e7ar E, Atila \u00dc, U\u00e7ar M, Akyol K (2021) Automated detection of COVID-19 disease using deep fused features from chest radiography images. Biomed Signal Process Control 69:102862. https:\/\/doi.org\/10.1016\/j.bspc.2021.102862","journal-title":"Biomed Signal Process Control"},{"key":"7653_CR22","doi-asserted-by":"publisher","first-page":"29367","DOI":"10.1007\/s11042-021-11153-y","volume":"80","author":"JOB Diniz","year":"2021","unstructured":"Diniz JOB, Quintanilha DBP, Santos Neto AC et al (2021) Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning. Multimed Tools Appl 80:29367\u201329399","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"7653_CR23","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s10278-021-00434-5","volume":"34","author":"\u00dc Budak","year":"2021","unstructured":"Budak \u00dc, \u00c7\u0131buk M, C\u00f6mert Z, \u015eeng\u00fcr A (2021) Efficient COVID-19 segmentation from CT slices exploiting semantic segmentation with integrated attention mechanism. J Digit Imaging 34(2):263\u2013272. https:\/\/doi.org\/10.1007\/s10278-021-00434-5","journal-title":"J Digit Imaging"},{"key":"7653_CR24","unstructured":"Yan Q, Wang B, Gong D, et al (2020) COVID-19 chest CT image segmentation--a deep convolutional neural network solution. arXiv Prepr https:\/\/arxiv.org\/abs\/2004.10987"},{"key":"7653_CR25","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"D-P Fan","year":"2020","unstructured":"Fan D-P, Zhou T, Ji G-P et al (2020) Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging 39:2626\u20132637","journal-title":"IEEE Trans Med Imaging"},{"key":"7653_CR26","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","volume":"30","author":"Y-H Wu","year":"2021","unstructured":"Wu Y-H, Gao S-H, Mei J et al (2021) Jcs: an explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113\u20133126","journal-title":"IEEE Trans Image Process"},{"key":"7653_CR27","unstructured":"Qiu Y, Liu Y, Li S, Xu J (2020) Miniseg: An extremely minimum network for efficient COVID-19 segmentation. arXiv Prepr https:\/\/arxiv.org\/pdf\/2004.09750"},{"key":"7653_CR28","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1002\/ima.22527","volume":"31","author":"T Zhou","year":"2021","unstructured":"Zhou T, Canu S, Ruan S (2021) Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int J Imaging Syst Technol 31:16\u201327","journal-title":"Int J Imaging Syst Technol"},{"key":"7653_CR29","unstructured":"Dataset COVID-19 CT segmentation dataset. http:\/\/medicalsegmentation.com\/covid19\/. Accessed 29 Jul 2021"},{"key":"7653_CR30","unstructured":"Siddique N, Sidike P, Elkin C, Devabhaktuni V (2020) U-Net and its variants for medical image segmentation: theory and applications. arXiv Prepr https:\/\/arxiv.org\/abs\/2011.01118"},{"key":"7653_CR31","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7653_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027","volume":"61","author":"M Byra","year":"2020","unstructured":"Byra M, Jarosik P, Szubert A et al (2020) Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed Signal Process Control 61:102027","journal-title":"Biomed Signal Process Control"},{"key":"7653_CR33","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1016\/j.bbe.2020.07.007","volume":"40","author":"A Khanna","year":"2020","unstructured":"Khanna A, Londhe ND, Gupta S, Semwal A (2020) A deep residual U-Net convolutional neural network for automated lung segmentation in computed tomography images. Biocybern Biomed Eng 40:1314\u20131327","journal-title":"Biocybern Biomed Eng"},{"key":"7653_CR34","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS (2020) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387","journal-title":"Neural Netw"},{"key":"7653_CR35","doi-asserted-by":"publisher","first-page":"6307","DOI":"10.1007\/s00521-020-05396-3","volume":"33","author":"A Farahani","year":"2021","unstructured":"Farahani A, Mohseni H (2021) Medical image segmentation using customized U-Net with adaptive activation functions. Neural Comput Appl 33:6307\u20136323","journal-title":"Neural Comput Appl"},{"key":"7653_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101885","volume":"89","author":"DT Kushnure","year":"2021","unstructured":"Kushnure DT, Talbar SN (2021) MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images. Comput Med Imaging Graph 89:101885","journal-title":"Comput Med Imaging Graph"},{"key":"7653_CR37","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"7653_CR38","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"7653_CR39","unstructured":"Yakubovskiy P (2019) Segmentation models. GitHub Repos. https:\/\/github.com\/qubvel\/segmentation_models. Accessed 15 Oct 2021"},{"key":"7653_CR40","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR et al (2016) Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35:1299\u20131312","journal-title":"IEEE Trans Med Imaging"},{"key":"7653_CR41","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks. arXiv Prepr https:\/\/arxiv.org\/pdf\/1411.1792"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07653-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07653-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07653-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:08:51Z","timestamp":1667866131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07653-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":41,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7653"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07653-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,8,6]]},"assertion":[{"value":"10 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declare that there is no any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}