{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T13:51:11Z","timestamp":1774705871305,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"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":["J Digit Imaging"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10278-021-00434-5","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T22:02:30Z","timestamp":1614981750000},"page":"263-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4082-383X","authenticated-orcid":false,"given":"\u00dcmit","family":"Budak","sequence":"first","affiliation":[]},{"given":"Musa","family":"\u00c7\u0131buk","sequence":"additional","affiliation":[]},{"given":"Zafer","family":"C\u00f6mert","sequence":"additional","affiliation":[]},{"given":"Abdulkadir","family":"\u015eeng\u00fcr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"434_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1148\/radiol.2020200257","volume":"295","author":"P Liu","year":"2020","unstructured":"Liu P, Tan X. 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology 2020;295:19. https:\/\/doi.org\/10.1148\/radiol.2020200257.","journal-title":"Radiology"},{"key":"434_CR2","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.12.20027185","author":"C Zheng","year":"2020","unstructured":"Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label. MedRxiv 2020. https:\/\/doi.org\/10.1101\/2020.03.12.20027185.","journal-title":"MedRxiv"},{"key":"434_CR3","doi-asserted-by":"publisher","unstructured":"Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for Radiologists on COVID-19: An Update\u2014Radiology Scientific Expert Panel. Radiology n.d.;0:200527. https:\/\/doi.org\/10.1148\/radiol.2020200527.","DOI":"10.1148\/radiol.2020200527"},{"key":"434_CR4","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 2020;140:109761. https:\/\/doi.org\/10.1016\/j.mehy.2020.109761.","journal-title":"Med Hypotheses"},{"key":"434_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfma.2020.04.007","author":"T-H Chang","year":"2020","unstructured":"Chang T-H, Wu J-L, Chang L-Y. Clinical characteristics and diagnostic challenges of pediatric COVID-19: A systematic review and meta-analysis. J Formos Med Assoc 2020. https:\/\/doi.org\/10.1016\/j.jfma.2020.04.007.","journal-title":"J Formos Med Assoc"},{"key":"434_CR6","doi-asserted-by":"publisher","unstructured":"Prokop M, van Everdingen W, van Rees Vellinga T, van Ufford J, St\u00f6ger L, Beenen L, et al. CO-RADS \u2013 A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation. Radiology n.d.;0:201473. https:\/\/doi.org\/10.1148\/radiol.2020201473.","DOI":"10.1148\/radiol.2020201473"},{"key":"434_CR7","doi-asserted-by":"publisher","first-page":"108972","DOI":"10.1016\/j.ejrad.2020.108972","volume":"126","author":"Z Chen","year":"2020","unstructured":"Chen Z, Fan H, Cai J, Li Y, Wu B, Hou Y, et al. High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. Eur J Radiol 2020;126:108972. https:\/\/doi.org\/10.1016\/j.ejrad.2020.108972.","journal-title":"Eur J Radiol"},{"key":"434_CR8","doi-asserted-by":"publisher","unstructured":"Cheng Z, Qin L, Cao Q, Dai J, Pan A, Yang W, et al. Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia. Radiol Infect Dis 2020. https:\/\/doi.org\/10.1016\/j.jrid.2020.04.004.","DOI":"10.1016\/j.jrid.2020.04.004"},{"key":"434_CR9","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jpha.2020.03.004","volume":"10","author":"C Shen","year":"2020","unstructured":"Shen C, Yu N, Cai S, Zhou J, Sheng J, Liu K, et al. Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019. J Pharm Anal 2020;10:123\u20139. https:\/\/doi.org\/10.1016\/j.jpha.2020.03.004.","journal-title":"J Pharm Anal"},{"key":"434_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2019.10.006","author":"M To\u011fa\u00e7ar","year":"2019","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models. IRBM 2019. https:\/\/doi.org\/10.1016\/j.irbm.2019.10.006.","journal-title":"IRBM"},{"key":"434_CR11","doi-asserted-by":"publisher","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya] U [Rajendra. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020:103792. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792.","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"434_CR12","doi-asserted-by":"publisher","unstructured":"Nour M, C\u00f6mert Z, Polat K. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Appl Soft Comput 2020:106580. https:\/\/doi.org\/10.1016\/j.asoc.2020.106580.","DOI":"10.1016\/j.asoc.2020.106580"},{"key":"434_CR13","doi-asserted-by":"publisher","unstructured":"Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 2020:103795. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795.","DOI":"10.1016\/j.compbiomed.2020.103795"},{"key":"434_CR14","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202003.0300.v1","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Behera SK. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020. https:\/\/doi.org\/10.20944\/preprints202003.0300.v1.","journal-title":"Preprints"},{"key":"434_CR15","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z. Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks 2020.","DOI":"10.1007\/s10044-021-00984-y"},{"key":"434_CR16","unstructured":"Hemdan EE-D, Shouman MA, Karar ME. COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images 2020."},{"key":"434_CR17","doi-asserted-by":"publisher","first-page":"103805","DOI":"10.1016\/j.compbiomed.2020.103805","volume":"121","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med 2020;121:103805. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103805.","journal-title":"Comput Biol Med"},{"key":"434_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Med. Image Comput. Comput. Interv. -- MICCAI 2015, Cham: Springer International Publishing; 2015, p. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"434_CR19","doi-asserted-by":"publisher","first-page":"109431","DOI":"10.1016\/j.mehy.2019.109431","volume":"134","author":"\u00dc Budak","year":"2020","unstructured":"Budak \u00dc, Guo Y, Tanyildizi E, \u015eeng\u00fcr A. Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. Med Hypotheses 2020;134:109431. https:\/\/doi.org\/10.1016\/j.mehy.2019.109431.","journal-title":"Med Hypotheses"},{"key":"434_CR20","unstructured":"Zhou T, Canu S, Ruan S. An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism 2020."},{"key":"434_CR21","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39:2481\u201395. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"434_CR22","doi-asserted-by":"publisher","first-page":"101734","DOI":"10.1016\/j.bspc.2019.101734","volume":"56","author":"E Ba\u015faran","year":"2020","unstructured":"Ba\u015faran E, C\u00f6mert Z, \u00c7elik Y. Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomed Signal Process Control 2020;56:101734. https:\/\/doi.org\/10.1016\/j.bspc.2019.101734.","journal-title":"Biomed Signal Process Control"},{"key":"434_CR23","doi-asserted-by":"publisher","unstructured":"Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. IEEE Rev Biomed Eng 2020:1. https:\/\/doi.org\/10.1109\/RBME.2020.2987975.","DOI":"10.1109\/RBME.2020.2987975"},{"key":"434_CR24","doi-asserted-by":"publisher","first-page":"e200082","DOI":"10.1148\/ryct.2020200082","volume":"2","author":"Y Cao","year":"2020","unstructured":"Cao Y, Xu Z, Feng J, Jin C, Han X, Wu H, et al. Longitudinal Assessment of COVID-19 Using a Deep Learning\u2013based Quantitative CT Pipeline: Illustration of Two Cases. Radiol Cardiothorac Imaging 2020;2:e200082. https:\/\/doi.org\/10.1148\/ryct.2020200082.","journal-title":"Radiol Cardiothorac Imaging"},{"key":"434_CR25","doi-asserted-by":"publisher","first-page":"e200075","DOI":"10.1148\/ryct.2020200075","volume":"2","author":"L Huang","year":"2020","unstructured":"Huang L, Han R, Ai T, Yu P, Kang H, Tao Q, et al. Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach. Radiol Cardiothorac Imaging 2020;2:e200075. https:\/\/doi.org\/10.1148\/ryct.2020200075.","journal-title":"Radiol Cardiothorac Imaging"},{"key":"434_CR26","doi-asserted-by":"publisher","unstructured":"Qi X, Jiang Z, YU Q, Shao C, Zhang H, Yue H, et al. Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study. MedRxiv 2020. https:\/\/doi.org\/10.1101\/2020.02.29.20029603.","DOI":"10.1101\/2020.02.29.20029603"},{"key":"434_CR27","unstructured":"Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis 2020."},{"key":"434_CR28","doi-asserted-by":"publisher","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology n.d.;0:200905. https:\/\/doi.org\/10.1148\/radiol.2020200905.","DOI":"10.1148\/radiol.2020200905"},{"key":"434_CR29","doi-asserted-by":"publisher","unstructured":"Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. MedRxiv 2020. https:\/\/doi.org\/10.1101\/2020.02.25.20021568","DOI":"10.1101\/2020.02.25.20021568"},{"key":"434_CR30","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.19.20039354","author":"S Jin","year":"2020","unstructured":"Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. MedRxiv 2020. https:\/\/doi.org\/10.1101\/2020.03.19.20039354.","journal-title":"MedRxiv"},{"key":"434_CR31","unstructured":"Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Lung Infection Quantification of COVID-19 in CT Images with Deep Learning 2020."},{"key":"434_CR32","doi-asserted-by":"publisher","unstructured":"Abdel-Basset M, Mohamed R, Elhoseny M, Chakrabortty RK, Ryan M. A hybrid COVID-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy. IEEE Access 2020:1. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990893.","DOI":"10.1109\/ACCESS.2020.2990893"},{"key":"434_CR33","unstructured":"Ga\u00e1l G, Maga B, Luk\u00e1cs A. Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation 2020."},{"key":"434_CR34","unstructured":"Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. Human-level CMR image analysis with deep fully convolutional networks 2017."},{"key":"434_CR35","doi-asserted-by":"publisher","first-page":"109426","DOI":"10.1016\/j.mehy.2019.109426","volume":"134","author":"\u00dc Budak","year":"2020","unstructured":"Budak \u00dc, C\u00f6mert Z, \u00c7\u0131buk M, \u015eeng\u00fcr A. DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images. Med Hypotheses 2020;134:109426. https:\/\/doi.org\/10.1016\/j.mehy.2019.109426.","journal-title":"Med Hypotheses"},{"key":"434_CR36","doi-asserted-by":"publisher","first-page":"105765","DOI":"10.1016\/j.asoc.2019.105765","volume":"85","author":"\u00dc Budak","year":"2019","unstructured":"Budak \u00dc, C\u00f6mert Z, Rashid ZN, \u015eeng\u00fcr A, \u00c7\u0131buk M. Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput 2019;85:105765. https:\/\/doi.org\/10.1016\/j.asoc.2019.105765.","journal-title":"Appl Soft Comput"},{"key":"434_CR37","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.3174\/ajnr.A5543","volume":"39","author":"G Zaharchuk","year":"2018","unstructured":"Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep Learning in Neuroradiology. Am J Neuroradiol 2018;39:1776\u201384. https:\/\/doi.org\/10.3174\/ajnr.A5543.","journal-title":"Am J Neuroradiol"},{"key":"434_CR38","unstructured":"Lee C-Y, Xie S, Gallagher P, Zhang Z, Tu Z. Deeply-Supervised Nets. In: Lebanon G, Vishwanathan SVN, editors. Proc. Eighteenth Int. Conf. Artif. Intell. Stat., vol. 38, San Diego, California, USA: PMLR; 2015, p. 562\u201370."},{"key":"434_CR39","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s13755-018-0057-x","volume":"6","author":"E Deniz","year":"2018","unstructured":"Deniz E, Seng\u00fcr A, Kadiroglu Z, Guo Y, Bajaj V, Budak \u00dc. Transfer learning based histopathologic image classification for breast cancer detection. Heal Inf Sci Syst 2018;6:18. https:\/\/doi.org\/10.1007\/s13755-018-0057-x.","journal-title":"Heal Inf Sci Syst"},{"key":"434_CR40","unstructured":"\u015eeng\u00fcr D. Investigation of the relationships of the students\u2019 academic level and gender with Covid-19 based anxiety and protective behaviors: A data mining approach 2020;15:93\u20139."},{"key":"434_CR41","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1109\/TMI.2013.2265805","volume":"32","author":"R Wolz","year":"2013","unstructured":"Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D. Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation. IEEE Trans Med Imaging 2013;32:1723\u201330. https:\/\/doi.org\/10.1109\/TMI.2013.2265805.","journal-title":"IEEE Trans Med Imaging"},{"key":"434_CR42","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.cmpb.2018.10.021","volume":"167","author":"Y Guo","year":"2018","unstructured":"Guo Y, Budak \u00dc, \u015eeng\u00fcr A. A novel retinal vessel detection approach based on multiple deep convolution neural networks. Comput Methods Programs Biomed 2018;167:43\u20138. https:\/\/doi.org\/10.1016\/j.cmpb.2018.10.021.","journal-title":"Comput Methods Programs Biomed"},{"key":"434_CR43","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.measurement.2018.05.003","volume":"125","author":"Y Guo","year":"2018","unstructured":"Guo Y, Budak \u00dc, Vespa LJ, Khorasani E, \u015eeng\u00fcr A. A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy. Measurement 2018;125:586\u201391. https:\/\/doi.org\/10.1016\/j.measurement.2018.05.003.","journal-title":"Measurement"},{"key":"434_CR44","doi-asserted-by":"publisher","unstructured":"Pesce E, Withey] S [Joseph, Ypsilantis P-P, Bakewell R, Goh V, Montana G. Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Med Image Anal 2019;53:26\u201338. https:\/\/doi.org\/10.1016\/j.media.2018.12.007.","DOI":"10.1016\/j.media.2018.12.007"},{"key":"434_CR45","unstructured":"Oktay O, Schlemper J, Folgoc L Le, Lee M, Heinrich M, Misawa K, et al. Attention U-Net: Learning Where to Look for the Pancreas 2018."},{"key":"434_CR46","doi-asserted-by":"publisher","unstructured":"Luong T, Pham H, Manning CD. Effective Approaches to Attention-based Neural Machine Translation. Proc. 2015 Conf. Empir. Methods Nat. Lang. Process., Lisbon, Portugal: Association for Computational Linguistics; 2015, p. 1412\u201321. https:\/\/doi.org\/10.18653\/v1\/D15-1166.","DOI":"10.18653\/v1\/D15-1166"},{"key":"434_CR47","unstructured":"Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate 2014."},{"key":"434_CR48","doi-asserted-by":"publisher","unstructured":"Britz D, Goldie A, Luong M-T, Le Q. Massive Exploration of Neural Machine Translation Architectures. Proc. 2017 Conf. Empir. Methods Nat. Lang. Process., Copenhagen, Denmark: Association for Computational Linguistics; 2017, p. 1442\u201351. https:\/\/doi.org\/10.18653\/v1\/D17-1151.","DOI":"10.18653\/v1\/D17-1151"},{"key":"434_CR49","doi-asserted-by":"crossref","unstructured":"Li R, Li M, Li J, Zhou Y. Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation 2019.","DOI":"10.1109\/ICIP.2019.8803101"},{"key":"434_CR50","doi-asserted-by":"crossref","unstructured":"Shankaranarayana SM, Ram K, Mitra K, Sivaprakasam M. Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks. In: Cardoso MJ, Arbel T, Melbourne A, Bogunovic H, Moeskops P, Chen X, et al., editors. Fetal, Infant Ophthalmic Med. Image Anal., Cham: Springer International Publishing; 2017, p. 168\u201376.","DOI":"10.1007\/978-3-319-67561-9_19"},{"key":"434_CR51","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1037\/0033-295X.84.4.327","volume":"84","author":"A Tversky","year":"1977","unstructured":"Tversky A. Features of similarity. Psychol Rev 1977;84:327\u201352. https:\/\/doi.org\/10.1037\/0033-295X.84.4.327.","journal-title":"Psychol Rev"},{"key":"434_CR52","doi-asserted-by":"publisher","unstructured":"Abraham N, Khan NM. A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th Int. Symp. Biomed. Imaging (ISBI 2019), 2019, p. 683\u20137. https:\/\/doi.org\/10.1109\/ISBI.2019.8759329.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"434_CR53","first-page":"61","volume":"13","author":"D \u015eeng\u00fcr","year":"2018","unstructured":"\u015eeng\u00fcr D, Turhan M. Prediction Of The Action Identification Levels Of Teachers Based On Organizational Commitment And Job Satisfaction By Using K-Nearest Neighbors Method 2018;13:61\u20138.","journal-title":"Prediction Of The Action Identification Levels Of Teachers Based On Organizational Commitment And Job Satisfaction By Using K-Nearest Neighbors Method"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00434-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00434-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00434-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T14:12:12Z","timestamp":1626703932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00434-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,5]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["434"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00434-5","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,5]]},"assertion":[{"value":"11 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no conflict to interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}