{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:11:32Z","timestamp":1776215492460,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09894-3","type":"journal-article","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T13:02:42Z","timestamp":1601989362000},"page":"5423-5447","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods"],"prefix":"10.1007","volume":"80","author":[{"given":"Huseyin","family":"Yasar","sequence":"first","affiliation":[]},{"given":"Murat","family":"Ceylan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,6]]},"reference":[{"key":"9894_CR1","unstructured":"https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/LIDC-IDRI (Access Time: 01 September 2020)"},{"key":"9894_CR2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ijid.2020.02.043","volume":"93","author":"F Albarello","year":"2020","unstructured":"Albarello F, Pianura E, Di Stefano F, Cristofaro M, Petrone A, Marchioni L, Palazzolo C, Schinin\u00e0 V, Nicastri E, Petrosillo N, Campioni P, Eskild P, Zumla A, Ippolito G, COVID 19 INMI Study Group (2020) 2019-novel Coronavirus severe adult respiratory distress syndrome in two cases in Italy: An uncommon radiological presentation. Int J Infect Dis 93:192\u2013197. https:\/\/doi.org\/10.1016\/j.ijid.2020.02.043","journal-title":"Int J Infect Dis"},{"issue":"3","key":"9894_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.2307\/2685209","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175\u2013185. https:\/\/doi.org\/10.2307\/2685209","journal-title":"Am Stat"},{"key":"9894_CR4","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"103795","author":"AA Ardakani","year":"2020","unstructured":"Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A (2020) 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 103795:121. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795","journal-title":"Comput Biol Med"},{"key":"9894_CR5","doi-asserted-by":"publisher","unstructured":"Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Clarke LP (2015) Data From LIDC-IDRI. The Cancer Imaging Archive 10:K9. https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX","DOI":"10.7937\/K9\/TCIA.2015.LO9QL9SX"},{"key":"9894_CR6","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato III S G, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Croft BY (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Med Phys 38:915\u2013931. https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med Phys"},{"key":"9894_CR7","unstructured":"Bloice MD, Holzinger A (2019) Patch augmentation: Towards efficient decision boundaries for neural networks. arXiv:1911.07922"},{"issue":"21","key":"9894_CR8","doi-asserted-by":"publisher","first-page":"4522","DOI":"10.1093\/bioinformatics\/btz259","volume":"35","author":"MD Bloice","year":"2019","unstructured":"Bloice MD, Roth PM, Holzinger A (2019) Biomedical image augmentation using Augmentor. Bioinformatics 35(21):4522\u20134524. https:\/\/doi.org\/10.1093\/bioinformatics\/btz259","journal-title":"Bioinformatics"},{"key":"9894_CR9","doi-asserted-by":"crossref","unstructured":"Bloice MD, Stocker C, Holzinger A (2017) Augmentor: An image augmentation library for machine learning. arXiv:1708.04680","DOI":"10.21105\/joss.00432"},{"issue":"10226","key":"9894_CR10","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/S0140-6736(20)30360-3","volume":"395","author":"H Chen","year":"2020","unstructured":"Chen H, Guo J, Wang C, Luo F, Yu X, Zhang W, Li J, Zhao D, Xu D, Gong Q, Liao J, Yang H, Hou W, Zhang Y (2020) Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. The Lancet 395(10226):809\u2013815. https:\/\/doi.org\/10.1016\/S0140-6736(20)30360-3","journal-title":"The Lancet"},{"issue":"3","key":"9894_CR11","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1016\/j.jfma.2020.02.007","volume":"119","author":"SC Cheng","year":"2020","unstructured":"Cheng SC, Chang YC, Chiang YLF, Chien YC, Cheng M, Yang CH, Huang CH, Hsu YN (2020) First case of Coronavirus Disease 2019 (COVID-19) pneumonia in Taiwan. J Formos Med Assoc 119(3):747\u2013751. https:\/\/doi.org\/10.1016\/j.jfma.2020.02.007","journal-title":"J Formos Med Assoc"},{"issue":"6","key":"9894_CR12","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging 26 (6):1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J Digit Imaging"},{"key":"9894_CR13","unstructured":"Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection, arXiv, 2020. https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"issue":"8","key":"9894_CR14","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TMI.2020.2996256","volume":"39","author":"Z Han","year":"2020","unstructured":"Han Z, Wei B, Hong Y, Li T, Cong J, Zhu X, Zhang W (2020) Accurate screening of COVID-19 using attention based deep 3D multiple instance learning. IEEE Trans Med Imaging 39(8):2584\u20132594. https:\/\/doi.org\/10.1109\/TMI.2020.2996256","journal-title":"IEEE Trans Med Imaging"},{"key":"9894_CR15","doi-asserted-by":"publisher","unstructured":"Hardalac F, Yasar H, Akyel A, Kutbay U (2020) A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-020-09005-2","DOI":"10.1007\/s11042-020-09005-2"},{"issue":"2","key":"9894_CR16","doi-asserted-by":"publisher","first-page":"508","DOI":"10.21037\/qims.2020.02.10","volume":"10","author":"X Hu","year":"2020","unstructured":"Hu X, Chen J, Jiang X, Tao S, Zhen Z, Zhou C, Wang J (2020) CT imaging of two cases of one family cluster 2019 novel coronavirus (2019-nCoV) pneumonia: inconsistency between clinical symptoms amelioration and imaging sign progression. Quant Imaging Med Surg 10(2):508. https:\/\/doi.org\/10.21037\/qims.2020.02.10","journal-title":"Quant Imaging Med Surg"},{"key":"9894_CR17","doi-asserted-by":"publisher","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","volume":"8","author":"S Hu","year":"2020","unstructured":"Hu S, Gao Y, Niu Z, Jiang Y, Li L, Xiao X, Ye H (2020) Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8:118869\u2013118883. https:\/\/doi.org\/10.1109\/ACCESS.2020.3005510","journal-title":"IEEE Access"},{"issue":"10223","key":"9894_CR18","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Li Zhang, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395 (10223):497\u2013506. https:\/\/doi.org\/10.1016\/S0140-6736(20)30183-5","journal-title":"The Lancet"},{"key":"9894_CR19","doi-asserted-by":"publisher","unstructured":"Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M (2020) Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics 1\u20138. https:\/\/doi.org\/10.1080\/07391102.2020.1788642","DOI":"10.1080\/07391102.2020.1788642"},{"key":"9894_CR20","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: In Advances in neural information processing systems, pp 1097\u20131105"},{"key":"9894_CR21","doi-asserted-by":"publisher","unstructured":"Li W, Cui H, Li K, Fang Y, Li S (2020) Chest computed tomography in children with COVID-19 respiratory infection. Pediatric Radiology. https:\/\/doi.org\/10.1007\/s00247-020-04656-7","DOI":"10.1007\/s00247-020-04656-7"},{"key":"9894_CR22","doi-asserted-by":"publisher","unstructured":"Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, Liu H (2020) Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease. Academic Radiology. https:\/\/doi.org\/10.1016\/j.acra.2020.03.003","DOI":"10.1016\/j.acra.2020.03.003"},{"issue":"6","key":"9894_CR23","doi-asserted-by":"publisher","first-page":"e79","DOI":"10.3346\/jkms.2020.35.e79","volume":"35","author":"J Lim","year":"2020","unstructured":"Lim J, Jeon S, Shin HY, Kim MJ, Seong YM, Lee WJ, Choe KW, Kang YM, Lee B, Park SJ (2020) Case of the index patient who caused tertiary transmission of COVID-19 infection in Korea: the application of lopinavir\/ritonavir for the treatment of COVID-19 infected pneumonia monitored by quantitative RT-PCR. J Korean Med Sci 35(6):e79. https:\/\/doi.org\/10.3346\/jkms.2020.35.e79","journal-title":"J Korean Med Sci"},{"key":"9894_CR24","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.clinimag.2020.02.008","volume":"63","author":"C Lin","year":"2020","unstructured":"Lin C, Ding Y, Xie B, Sun Z, Li X, Chen Z, Niu M (2020) Asymptomatic novel coronavirus pneumonia patient outside Wuhan: The value of CT images in the course of the disease. Clin Imaging 63:7\u20139. https:\/\/doi.org\/10.1016\/j.clinimag.2020.02.008","journal-title":"Clin Imaging"},{"key":"9894_CR25","doi-asserted-by":"publisher","first-page":"108941","DOI":"10.1016\/j.ejrad.2020.108941","volume":"126","author":"KC Liu","year":"2020","unstructured":"Liu KC, Xu P, Lv WF, Qiu XH, Yao JL, Jin-Feng G (2020) CT manifestations of coronavirus disease-2019: a retrospective analysis of 73 cases by disease severity. Eur J Radiol 126:108941. https:\/\/doi.org\/10.1016\/j.ejrad.2020.108941","journal-title":"Eur J Radiol"},{"key":"9894_CR26","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10916-020-1536-6","volume":"44","author":"JB Long","year":"2020","unstructured":"Long JB, Ehrenfeld JM (2020) The role of augmented intelligence (ai) in detecting and preventing the spread of novel coronavirus. J Med Syst 44:59. https:\/\/doi.org\/10.1007\/s10916-020-1536-6","journal-title":"J Med Syst"},{"issue":"1","key":"9894_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala T, Pietik\u00e4inen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51\u201359. https:\/\/doi.org\/10.1016\/0031-3203(95)00067-4","journal-title":"Pattern Recogn"},{"key":"9894_CR28","doi-asserted-by":"publisher","unstructured":"Ornek AH, Ceylan M (2019) Comparison of traditional transformations for data augmentation in deep learning of medical thermography. In: International Conference on Telecommunications and Signal Processing (TSP). https:\/\/doi.org\/10.1109\/TSP.2019.8769068, pp 191\u2013194","DOI":"10.1109\/TSP.2019.8769068"},{"key":"9894_CR29","doi-asserted-by":"publisher","first-page":"103044","DOI":"10.1016\/j.infrared.2019.103044","volume":"103","author":"AH Ornek","year":"2019","unstructured":"Ornek AH, Ceylan M, Ervural S (2019) Health status detection of neonates using infrared thermography and deep convolutional neural networks. Infrared Physics & Technology 103:103044. https:\/\/doi.org\/10.1016\/j.infrared.2019.103044","journal-title":"Infrared Physics & Technology"},{"issue":"8","key":"9894_CR30","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","volume":"39","author":"X Ouyang","year":"2020","unstructured":"Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, Shi F (2020) Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 39(8):2595\u20132605. https:\/\/doi.org\/10.1109\/TMI.2020.2995508","journal-title":"IEEE Trans Med Imaging"},{"key":"9894_CR31","doi-asserted-by":"publisher","unstructured":"Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, Hu Q, Xia L (2020) Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. European radiology. https:\/\/doi.org\/10.1007\/s00330-020-06731-x","DOI":"10.1007\/s00330-020-06731-x"},{"key":"9894_CR32","doi-asserted-by":"publisher","unstructured":"Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK (2020) Deep Transfer Learning based Classification Model for COVID-19 Disease. IRBM. https:\/\/doi.org\/10.1016\/j.irbm.2020.05.003","DOI":"10.1016\/j.irbm.2020.05.003"},{"issue":"5","key":"9894_CR33","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s00259-020-04734-w","volume":"47","author":"C Qin","year":"2020","unstructured":"Qin C, Liu F, Yen TC, Lan X (2020) 18 F-FDG PET\/CT findings of COVID-19: a series of four highly suspected cases. European Journal of Nuclear Medicine and Molecular Imaging 47(5):1281\u20131286. https:\/\/doi.org\/10.1007\/s00259-020-04734-w","journal-title":"European Journal of Nuclear Medicine and Molecular Imaging"},{"key":"9894_CR34","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3233\/SHTI200481","volume":"272","author":"A Sakagianni","year":"2020","unstructured":"Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V (2020) Setting up an easy-to-use machine learning pipeline for medical decision support: Case study for COVID-19 diagnosis based on deep learning with CT scans. Stud Health Technol Inform 272:13\u201316. https:\/\/doi.org\/10.3233\/SHTI200481","journal-title":"Stud Health Technol Inform"},{"key":"9894_CR35","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. https:\/\/doi.org\/10.1109\/CVPR.2018.00474, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"4","key":"9894_CR36","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s12098-020-03263-6","volume":"87","author":"T Singhal","year":"2020","unstructured":"Singhal T (2020) A review of coronavirus disease-2019 (COVID-19). The Indian J Pediatr 87 (4):281\u2013286. https:\/\/doi.org\/10.1007\/s12098-020-03263-6","journal-title":"The Indian J Pediatr"},{"key":"9894_CR37","doi-asserted-by":"publisher","unstructured":"Shen C, Yu N, Cai S, Zhou J, Sheng J, Liu K, Zhouf H, Guoa Y, Niu G Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019. Journal of Pharmaceutical Analysis, https:\/\/doi.org\/10.1016\/j.jpha.2020.03.004, vol 2020","DOI":"10.1016\/j.jpha.2020.03.004"},{"key":"9894_CR38","unstructured":"Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Autom Remote Control 25"},{"key":"9894_CR39","doi-asserted-by":"publisher","unstructured":"Xia W, Shao J, Guo Y, Peng X, Li Z, Hu D (2020) Clinical and CT features in pediatric patients with COVID-19 infection: Different points from adults. Pediatric Pulmonology. https:\/\/doi.org\/10.1002\/ppul.24718","DOI":"10.1002\/ppul.24718"},{"key":"9894_CR40","doi-asserted-by":"publisher","first-page":"12007","DOI":"10.1007\/s11042-019-08566","volume":"79","author":"Z Xing","year":"2020","unstructured":"Xing Z, Jia H (2020) An improved thermal exchange optimization based GLCM for multi-level image segmentation. Multimed Tools Appl 79:12007\u201312040. https:\/\/doi.org\/10.1007\/s11042-019-08566","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"9894_CR41","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jinf.2020.02.017","volume":"80","author":"YH Xu","year":"2020","unstructured":"Xu YH, Dong JH, An WM, Lv XY, Yin XP, Zhang JZ, Dong L, Ma X, Zhang HJ, Gao BL (2020) Clinical and computed tomographic imaging features of Novel Coronavirus Pneumonia caused by SARS-CoV-2. J Infect 80(4):394\u2013400. https:\/\/doi.org\/10.1016\/j.jinf.2020.02.017","journal-title":"J Infect"},{"issue":"5","key":"9894_CR42","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s00259-020-04735-9","volume":"47","author":"X Xu","year":"2020","unstructured":"Xu X, Yu C, Qu J, Zhang L, Jiang S, Huang D, Chen B, Zhang Z, Guan W, Ling Z, Jiang R, Hu T, Ding Y, Lin L, Gan Q, Luo L, Tang X, Liu J (2020) Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging 47(5):1275\u20131280. https:\/\/doi.org\/10.1007\/s00259-020-04735-9","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"9894_CR43","doi-asserted-by":"publisher","first-page":"e0229651","DOI":"10.1371\/journal.pone.0229651","volume":"15","author":"W Yang","year":"2020","unstructured":"Yang W, Cai L, Wu F (2020) Image segmentation based on gray level and local relative entropy two dimensional histogram. Plos one 15(3):e0229651. https:\/\/doi.org\/10.1371\/journal.pone.0229651","journal-title":"Plos one"},{"issue":"3","key":"9894_CR44","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1166\/jmihi.2016.1737","volume":"6","author":"H Yasar","year":"2016","unstructured":"Yasar H, Ceylan M (2016) A novel approach for reduction of breast tissue density effects on normal and abnormal masses classification. J Med Imaging Health Infor 6(3):710\u2013717. https:\/\/doi.org\/10.1166\/jmihi.2016.1737","journal-title":"J Med Imaging Health Infor"},{"key":"9894_CR45","unstructured":"Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-Dataset: a CT scan dataset about COVID-19. arXiv:2003.13865"},{"key":"9894_CR46","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1056\/NEJMoa2001017","volume":"382","author":"N Zhu","year":"2020","unstructured":"Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W (2020) A novel coronavirus from patients with pneumonia in China, 2019. New England J Med 382:727\u2013733. https:\/\/doi.org\/10.1056\/NEJMoa2001017","journal-title":"New England J Med"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09894-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09894-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09894-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T07:04:47Z","timestamp":1633503887000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09894-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,6]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9894"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09894-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,6]]},"assertion":[{"value":"19 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"Dr. Ceylan declares that he has no conflict of interest. Mr. Yasar declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}