{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:54:09Z","timestamp":1770274449554,"version":"3.49.0"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"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":[[2022,6]]},"DOI":"10.1007\/s11042-022-11913-4","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T19:03:44Z","timestamp":1647371024000},"page":"21471-21501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Perspective of AI system for COVID-19 detection using chest images: a review"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1518-3332","authenticated-orcid":false,"given":"Dolly","family":"Das","sequence":"first","affiliation":[]},{"given":"Saroj Kumar","family":"Biswas","sequence":"additional","affiliation":[]},{"given":"Sivaji","family":"Bandyopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"11913_CR1","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","volume":"51","author":"A Abbas","year":"2021","unstructured":"Abbas A, Abdelsamea MM, Gaber MM (2021) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 51:854\u2013864. https:\/\/doi.org\/10.1007\/s10489-020-01829-7","journal-title":"Appl Intell"},{"key":"11913_CR2","doi-asserted-by":"publisher","first-page":"101607","DOI":"10.1016\/j.tmaid.2020.101607","volume":"36","author":"T Ahmad","year":"2020","unstructured":"Ahmad T, Khan M, Haroon THM, Nasir S, Hui J, Bonilla-Aldana DK, Rodriguez-Morales AJ (2020) COVID-19: zoonotic aspects. Travel Med Infect Dis 36:101607. https:\/\/doi.org\/10.1016\/j.tmaid.2020.101607","journal-title":"Travel Med Infect Dis"},{"issue":"2","key":"11913_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2):32\u201340. https:\/\/doi.org\/10.1148\/radiol.2020200642","journal-title":"Radiology"},{"key":"11913_CR4","doi-asserted-by":"publisher","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AAS, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. 8(3):Electronics, 292 1-66. https:\/\/doi.org\/10.3390\/electronics8030292","DOI":"10.3390\/electronics8030292"},{"key":"11913_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"121","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 121:1\u20139. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795","journal-title":"Comput Biol Med"},{"issue":"4","key":"11913_CR6","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1152\/physiolgenomics.00029.2020","volume":"52","author":"AS Aryal","year":"2020","unstructured":"Aryal AS, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight COVID-19. AI Mach Learn Understand Biol Processes 52(4):200\u2013202. https:\/\/doi.org\/10.1152\/physiolgenomics.00029.2020","journal-title":"AI Mach Learn Understand Biol Processes"},{"key":"11913_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) CNN-based transfer learning\u2013BiLSTM network: a novel approach for COVID-19 infection detection. Appl Soft Comput 98:1\u201312. https:\/\/doi.org\/10.1016\/j.asoc.2020.106912","journal-title":"Appl Soft Comput"},{"issue":"5","key":"11913_CR8","doi-asserted-by":"publisher","first-page":"2000062","DOI":"10.2807\/1560-7917.ES.2020.25.5.2000062","volume":"25","author":"JA Backer","year":"2020","unstructured":"Backer JA, Klinkenberg D, Wallinga J (2020) Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China. Euro Surveill. 25(5):2000062. https:\/\/doi.org\/10.2807\/1560-7917.ES.2020.25.5.2000062","journal-title":"Euro Surveill."},{"issue":"14","key":"11913_CR9","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1001\/jama.2020.2565","volume":"323","author":"Y Bai","year":"2020","unstructured":"Bai Y, Yao L, Wei T, Tian F, Jin DY, Chen L, Wang M (2020) Presumed asymptomatic carrier transmission of COVID-19. Jama 323(14):1406\u20131407. https:\/\/doi.org\/10.1001\/jama.2020.2565","journal-title":"Jama"},{"issue":"2","key":"11913_CR10","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1148\/radiol.2020200823","volume":"296","author":"HX Bai","year":"2020","unstructured":"Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Pan I, Shi LB, Wang DC, Mei J, Jiang XL, Zeng QH, Egglin TK, Hu PF, Agarwal S, Xie F, Li S, Healey T, Atalay MK (2020) Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology 296(2):46\u201354. https:\/\/doi.org\/10.1148\/radiol.2020200823","journal-title":"Radiology"},{"key":"11913_CR11","unstructured":"Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) classification using CT images by machine learning methods. CoRR. Abs\/2003.09424: 1-10"},{"key":"11913_CR12","unstructured":"Sharon Begley (2020) Covid-19 testing issues could sink plans to re-open the country Might CT scans help? https:\/\/www.statnews.com\/2020\/04\/16\/ct-scans-alternative-to-inaccurate-coronavirus-tests\/. Accessed on 30-4-2020"},{"issue":"3","key":"11913_CR13","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1148\/radiol.2020200463","volume":"295","author":"A Bernheim","year":"2020","unstructured":"Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, Diao K, Lin B, Zhu X, Li K, Li S, Shan H, Jacobi A, Chung M (2020) Chest CT findings in coronavirus Disease-19 (COVID-19): relationship to duration of infection. Radiology 295(3):685\u2013691. https:\/\/doi.org\/10.1148\/radiol.2020200463","journal-title":"Radiology"},{"issue":"2","key":"11913_CR14","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1148\/radiol.2020201237","volume":"296","author":"D Caruso","year":"2020","unstructured":"Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C, Guido G, Bracci B, de Dominicis C, Laghi A (2020) Chest CT features of COVID-19 in Rome, Italy. Radiology 296(2):79\u201385. https:\/\/doi.org\/10.1148\/radiol.2020201237","journal-title":"Radiology"},{"key":"11913_CR15","doi-asserted-by":"publisher","unstructured":"Chan JFW, Yuan S, Kok KH, To KKW, Chu H, Yang J, Xing F, Liu J, Yip CCY, Poon RWS, Tsoi HW, Lo SKF, Chan KH, Poon VKM, Chan WM, Ip JD, Cai JP, Cheng VCC, Chen H, \u2026 Yuen KY (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395(10223):514\u2013523. https:\/\/doi.org\/10.1016\/S0140-6736(20)30154-9","DOI":"10.1016\/S0140-6736(20)30154-9"},{"issue":"5","key":"11913_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1128\/JCM.00310-20","volume":"58","author":"JF Chan","year":"2020","unstructured":"Chan JF, Yip CC, To KK, Tang TH, Wong SC, Leung KH, Fung AY, Ng AC, Zou Z, Tsoi HW, Choi GK, Tam AR, Cheng VC, Chan KH, Tsang OT, Yuen KY (2020) Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp\/Hel real-time reverse transcription-polymerase chain reaction assay validated in vitro and with clinical specimens. J Clin Microbiol 58(5):1\u201310. https:\/\/doi.org\/10.1128\/JCM.00310-20","journal-title":"J Clin Microbiol"},{"issue":"2\u20133","key":"11913_CR17","doi-asserted-by":"publisher","first-page":"147","DOI":"10.4103\/ijmr.IJMR_519_20","volume":"151","author":"P Chatterjee","year":"2020","unstructured":"Chatterjee P, Nagi N, Agarwal A, Das B, Banerjee S, Sarkar S, Gupta N, Gangakhedkar RR (2020) The 2019 novel coronavirus disease (COVID-19) pandemic: a review of the current evidence. Indian J Med Res 151(2\u20133):147\u2013159. https:\/\/doi.org\/10.4103\/ijmr.IJMR_519_20","journal-title":"Indian J Med Res"},{"issue":"10223","key":"11913_CR18","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","volume":"395","author":"N Chen","year":"2020","unstructured":"Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223):507\u2013513. https:\/\/doi.org\/10.1016\/S0140-6736(20)30211-7","journal-title":"Lancet"},{"key":"11913_CR19","doi-asserted-by":"publisher","unstructured":"Chowdhury NK, Kabir MA, Rahman M, Rezoana N (2020) Ecovnet: an ensemble of deep convolutional neural networks based on efficientnet to detect covid-19 from chest X-rays, arXiv:2009.11850v2 https:\/\/doi.org\/10.7717\/peerj-cs.551, 7","DOI":"10.7717\/peerj-cs.551"},{"key":"11913_CR20","unstructured":"Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: interim guidance (2020). https:\/\/apps.who.int\/iris\/handle\/10665\/331446. Accessed on 01-05-2020"},{"issue":"39","key":"11913_CR21","doi-asserted-by":"publisher","first-page":"20285","DOI":"10.2807\/ese.17.39.20285-en","volume":"17","author":"VM Corman","year":"2012","unstructured":"Corman VM, Eckerle I, Bleicker T, Zaki A, Landt O, Eschbach-Bludau M, Boheemen SV, Gopal R, Ballhause M, Bestebroer TM, Muth D, M\u00fcller MA, Drexler JF, Zambon MC, Osterhaus ADME, Fouchier RM, Drosten C (2012) Detection of a novel human coronavirus by real-time reverse-transcription polymerase chain reaction. Euro Surveill 17(39):20285. https:\/\/doi.org\/10.2807\/ese.17.39.20285-en","journal-title":"Euro Surveill"},{"key":"11913_CR22","doi-asserted-by":"publisher","first-page":"104742","DOI":"10.1016\/j.antiviral.2020.104742","volume":"176","author":"B Coutard","year":"2020","unstructured":"Coutard B, Valle C, de Lamballerie X, Canard B, Seidah NG, Decroly E (2020) The spike glycoprotein of the new coronavirus 2019-nCoV contains a furin-like cleavage site absent in CoV of the same clade. Antivir Res 176:104742. https:\/\/doi.org\/10.1016\/j.antiviral.2020.104742","journal-title":"Antivir Res"},{"key":"11913_CR23","doi-asserted-by":"publisher","unstructured":"Dansana D, Kumar R, Bhattacharjee A, Hemanth DJ, Gupta D, Khanna A, Castillo O (2020) Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm. Soft Comput 1-9. https:\/\/doi.org\/10.1007\/s00500-020-05275-y, 1, 9","DOI":"10.1007\/s00500-020-05275-y"},{"key":"11913_CR24","doi-asserted-by":"publisher","first-page":"101755","DOI":"10.1016\/j.tmaid.2020.101755","volume":"37","author":"K Dhama","year":"2020","unstructured":"Dhama K, Patel SK, Pathak M, Yatoo MI, Tiwari R, Malik YS, Singh R, Sah R, Rabaan AA, Bonilla-Aldana DK, Rodriguez-Morales AJ (2020) An update on SARS-CoV-2\/COVID-19 with particular reference to its clinical pathology, pathogenesis, immunopathology and mitigation strategies. Travel Med Infect Dis 37:101755. https:\/\/doi.org\/10.1016\/j.tmaid.2020.101755","journal-title":"Travel Med Infect Dis"},{"key":"11913_CR25","doi-asserted-by":"publisher","unstructured":"Dhama K, Khan S, Tiwari R, Sircar S, Bhat S, Malik YS, Singh KP, Chaicumpa W, Bonilla-Aldana DK, Rodriguez-Morales AJ (2020) Coronavirus disease 2019-COVID-19. Clin Microbiol Rev 33(4). https:\/\/doi.org\/10.1128\/CMR.00028-20","DOI":"10.1128\/CMR.00028-20"},{"issue":"1","key":"11913_CR26","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1148\/radiol.2020200323","volume":"295","author":"YN Duan","year":"2020","unstructured":"Duan YN, Qin J (2020) Pre-and posttreatment chest CT findings: 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology 295(1):21. https:\/\/doi.org\/10.1148\/radiol.2020200323","journal-title":"Radiology"},{"issue":"6","key":"11913_CR27","doi-asserted-by":"publisher","first-page":"e0235187 1-18","DOI":"10.1371\/journal.pone.0235187","volume":"15","author":"MA Elaziz","year":"2020","unstructured":"Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT (2020) New machine learning method for image-based diagnosis of COVID-19. PLOS ONE 15(6):e0235187 1-18. https:\/\/doi.org\/10.1371\/journal.pone.0235187","journal-title":"PLOS ONE"},{"key":"11913_CR28","doi-asserted-by":"publisher","unstructured":"Evangelin MP, Krishna BG, Raga SY, Vamsi GK, Zakeer S, Raju KN (2020) A review: outbreak of Corona. Journal of medical biomedical and applied sciences. 8(4):354-357. https:\/\/doi.org\/10.15520\/jmbas.v8i4.217","DOI":"10.15520\/jmbas.v8i4.217"},{"key":"11913_CR29","unstructured":"Gaillard F, Normal chest x-ray. Case study, Radiopaedia.org. https:\/\/radiopaedia.org\/cases\/8304.Accessed on 27\u201309- 2021"},{"key":"11913_CR30","unstructured":"Global Situation. World Health Organization (WHO) Coronavirus (COVID-19) Dashboard. https:\/\/covid19.who.int\/. Accessed on 28-09-2021"},{"key":"11913_CR31","doi-asserted-by":"publisher","unstructured":"Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ, Zeng G, Yuen KY, Chen RC, Tang CL, Wang T, Chen PY, Xiang J, \u2026 Zhong NS (2020) Clinical characteristics of 2019 novel coronavirus infection in China. N Engl J Med 382(18):1708\u20131720. https:\/\/doi.org\/10.1056\/NEJMoa2002032","DOI":"10.1056\/NEJMoa2002032"},{"key":"11913_CR32","doi-asserted-by":"publisher","unstructured":"Gupta R, Pal SK (2020) Trend analysis and forecasting of COVID-19 outbreak in India. medRxiv 1-19. https:\/\/doi.org\/10.1101\/2020.03.26.20044511","DOI":"10.1101\/2020.03.26.20044511"},{"key":"11913_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cell.2020.02.052","volume":"181","author":"M Hoffmann","year":"2020","unstructured":"Hoffmann M, Kleine-Weber H, Kr\u00fcger N, Mueller MA, Drosten C, P\u00f6hlmann S (2020) The novel coronavirus 2019 (2019-nCoV) uses the SARS-coronavirus receptor ACE2 and the cellular protease TMPRSS2 for entry into target cells. Cell 181:1\u201323. https:\/\/doi.org\/10.1016\/j.cell.2020.02.052","journal-title":"Cell"},{"key":"11913_CR34","doi-asserted-by":"publisher","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N (2020) COVID-19 detection through transfer learning using multimodal imaging data. IEEE Access 8:149808\u2013149824. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016780","journal-title":"IEEE Access"},{"key":"11913_CR35","doi-asserted-by":"publisher","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, \u2026 Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497\u2013506. https:\/\/doi.org\/10.1016\/S0140-6736(20)30183-5","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"11913_CR36","unstructured":"India Situation. World Health Organization (WHO). https:\/\/covid19.who.int\/region\/searo\/country\/in . Accessed on 28-09-2021"},{"key":"11913_CR37","doi-asserted-by":"crossref","unstructured":"Jadhav S, Deng G, Zawin M, Kaufman AE (2021) COVID-view: diagnosis of COVID-19 using chest CT. arXiv preprint arXiv:2108.03799","DOI":"10.1109\/TVCG.2021.3114851"},{"issue":"4","key":"11913_CR38","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1016\/j.bbe.2020.08.008","volume":"40","author":"G Jain","year":"2020","unstructured":"Jain G, Mittal D, Thakur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybernetics Biomed Eng 40(4):1391\u20131405. https:\/\/doi.org\/10.1016\/j.bbe.2020.08.008","journal-title":"Biocybernetics Biomed Eng"},{"issue":"15","key":"11913_CR39","doi-asserted-by":"publisher","first-page":"5682","DOI":"10.1080\/07391102.2020.1788642","volume":"39","author":"A Jaiswal","year":"2020","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. J Biomol Struct Dyn 39(15):5682\u20135689. https:\/\/doi.org\/10.1080\/07391102.2020.1788642","journal-title":"J Biomol Struct Dyn"},{"issue":"4","key":"11913_CR40","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/jmv.25682","volume":"92","author":"W Ji","year":"2020","unstructured":"Ji W, Wang W, Zhao X, Zai J, Li X (2020) Cross-species transmission of the newly identified coronavirus 2019-nCoV. J Med Virol 92(4):433\u2013440. https:\/\/doi.org\/10.1002\/jmv.25682","journal-title":"J Med Virol"},{"key":"11913_CR41","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s11760-020-01820-2","volume":"15","author":"KC Kamal","year":"2021","unstructured":"Kamal KC, Yin Z, Wu M, Wu Z (2021) Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. SIViP 15:959\u2013966. https:\/\/doi.org\/10.1007\/s11760-020-01820-2","journal-title":"SIViP"},{"key":"11913_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtbi.2021.110621","volume":"517","author":"R Ke","year":"2021","unstructured":"Ke R, Romero-Severson E, Sanche S, Hengartner N (2021) Estimating the reproductive number R0 of SARS-CoV-2 in the United States and eight European countries and implications for vaccination. J Theor Biol 517:1\u201310. https:\/\/doi.org\/10.1016\/j.jtbi.2021.110621","journal-title":"J Theor Biol"},{"key":"11913_CR43","unstructured":"Kitchener P (2013), Importance of medical imaging, vision XRay group. https:\/\/www.xray.com.au\/importance-of-medical-imaging\/. Accessed on: 09-09-2021"},{"key":"11913_CR44","unstructured":"Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases (2020). https:\/\/www.who.int\/publications\/i\/item\/10665-331501. Accessed on 03-05-2020"},{"key":"11913_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chaos.2020.110059","volume":"139","author":"S Lalmuanawma","year":"2020","unstructured":"Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos, Solitons Fractals 139:1\u201312. https:\/\/doi.org\/10.1016\/j.chaos.2020.110059","journal-title":"Chaos, Solitons Fractals"},{"issue":"3","key":"11913_CR46","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.jmii.2020.02.001","volume":"53","author":"PI Lee","year":"2020","unstructured":"Lee PI, Hsueh PR (2020) Emerging threats from zoonotic coronaviruses-from SARS and MERS to 2019-nCoV. J Microbiol Immunol Infect 53(3):365\u2013367. https:\/\/doi.org\/10.1016\/j.jmii.2020.02.001","journal-title":"J Microbiol Immunol Infect"},{"issue":"4","key":"11913_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/jpm10040213","volume":"10","author":"KS Lee","year":"2020","unstructured":"Lee KS, Kim JY, Jeon ET, Choi WS, Kim NH, Lee KY (2020) Evaluation of scalability and degree of fine-tuning of deep convolutional neural networks for COVID-19 screening on chest X-ray images using explainable deep-learning algorithm. J Pers Med 10(4):1\u201314. https:\/\/doi.org\/10.3390\/jpm10040213","journal-title":"J Pers Med"},{"issue":"1","key":"11913_CR48","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1148\/radiol.2020200236","volume":"295","author":"J Lei","year":"2020","unstructured":"Lei J, Li J, Li X, Qi X (2020) CT imaging of the 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology 295(1):18. https:\/\/doi.org\/10.1148\/radiol.2020200236","journal-title":"Radiology"},{"key":"11913_CR49","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1038\/s41564-020-0688-y","volume":"5","author":"M Letko","year":"2020","unstructured":"Letko M, Marzi A, Munster V (2020) Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat Microbiol 5:562\u2013569. https:\/\/doi.org\/10.1038\/s41564-020-0688-y","journal-title":"Nat Microbiol"},{"issue":"6","key":"11913_CR50","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.2214\/AJR.20.22954","volume":"214","author":"Y Li","year":"2020","unstructured":"Li Y, Xia L (2020) Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. Am J Roentgenol 214(6):1280\u20131286. https:\/\/doi.org\/10.2214\/AJR.20.22954","journal-title":"Am J Roentgenol"},{"issue":"3","key":"11913_CR51","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/s11427-020-1645-7","volume":"63","author":"X Li","year":"2020","unstructured":"Li X, Song Y, Wong G, Cui J (2020) Bat origin of a new human coronavirus: there and back again. Science China. Life Sci 63(3):461\u2013462. https:\/\/doi.org\/10.1007\/s11427-020-1645-7","journal-title":"Life Sci"},{"issue":"6","key":"11913_CR52","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1002\/jmv.25731","volume":"92","author":"X Li","year":"2020","unstructured":"Li X, Zai J, Zhao Q, Nie Q, Li Y, Foley BT, Chaillon A (2020) Evolutionary history, potential intermediate animal host, and cross-species analyses of SARS-CoV-2. J Med Virol 92(6):602\u2013611. https:\/\/doi.org\/10.1002\/jmv.25731","journal-title":"J Med Virol"},{"key":"11913_CR53","doi-asserted-by":"publisher","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia Juan, Xia Jia (2020) Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 1\u201316. https:\/\/doi.org\/10.1148\/radiol.2020200905","DOI":"10.1148\/radiol.2020200905"},{"issue":"6","key":"11913_CR54","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jinf.2020.03.005","volume":"80","author":"K Liu","year":"2020","unstructured":"Liu K, Chen Y, Lin R, Han K (2020) Clinical features of COVID-19 in elderly patients: a comparison with young and middle-aged patients. J Infect 80(6):14\u201318. https:\/\/doi.org\/10.1016\/j.jinf.2020.03.005","journal-title":"J Infect"},{"key":"11913_CR55","doi-asserted-by":"publisher","unstructured":"Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, Wang W, Song H, Huang B, Zhu N, Bi Y, Ma X, Zhan F, Wang L, Hu T, Zhou H, Hu Z, Zhou W, Zhao L, \u2026 Tan W (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395(10224):565\u2013574. https:\/\/doi.org\/10.1016\/S0140-6736(20)30251-8","DOI":"10.1016\/S0140-6736(20)30251-8"},{"issue":"1","key":"11913_CR56","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1080\/01652176.2020.1727993","volume":"40","author":"YS Malik","year":"2020","unstructured":"Malik YS, Sircar S, Bhat S, Sharun K, Dhama K, Dadar M, Tiwari R, Chaicumpa W (2020) Emerging novel coronavirus (2019-nCoV)\u2014current scenario, evolutionary perspective based on genome analysis and recent developments. Vet Q 40(1):68\u201376. https:\/\/doi.org\/10.1080\/01652176.2020.1727993","journal-title":"Vet Q"},{"key":"11913_CR57","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.cegh.2020.06.012","volume":"9","author":"S Marimuthu","year":"2021","unstructured":"Marimuthu S, Joy M, Malavika B, Nadaraj A, Asirvatham ES, Jeyaseelan L (2021) Modelling of reproduction number for COVID-19 in India and high incidence states. Clin Epidemiol Global Health 9:57\u201361. https:\/\/doi.org\/10.1016\/j.cegh.2020.06.012","journal-title":"Clin Epidemiol Global Health"},{"issue":"1510","key":"11913_CR58","first-page":"12","volume":"133","author":"DR Murdoch","year":"2020","unstructured":"Murdoch DR, French NP (2020) COVID-19: another infectious disease emerging at the animal-human interface. New Zealand Med J (Online) 133(1510):12\u201315","journal-title":"New Zealand Med J (Online)"},{"key":"11913_CR59","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","volume":"24","author":"A Narin","year":"2021","unstructured":"Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Applic 24:1207\u20131220. https:\/\/doi.org\/10.1007\/s10044-021-00984-y","journal-title":"Pattern Anal Applic"},{"issue":"1","key":"11913_CR60","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034","volume":"2","author":"MY Ng","year":"2020","unstructured":"Ng MY, Lee EY, Yang J, Yang F, Li X, Wang H, Lui MMS, Lo CSY, Leung B, Khong PL, Hui CKM, Yuen KY, Kuo MD (2020) Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol: Card Imaging 2(1):e200034. https:\/\/doi.org\/10.1148\/ryct.2020200034","journal-title":"Radiol: Card Imaging"},{"key":"11913_CR61","unstructured":"Kirti Pandey (2021) COVID Vaccines approved in India: Covishield, Covaxin, Sputnik V, Moderna; here's all you wish to know. https:\/\/www.timesnownews.com\/health\/article\/covid-vaccines-approved-in-india-covishield-covaxin-sputnik-v-moderna-heres-all-you-wish-to-know\/778482. Accessed on 03-07-2021"},{"key":"11913_CR62","unstructured":"Q&A Detail (2020). https:\/\/www.who.int\/news-room\/q-a-detail. Accessed on 30-4-2020"},{"issue":"1","key":"11913_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1148\/ryct.2020200033","volume":"2","author":"L Qian","year":"2020","unstructured":"Qian L, Yu J, Shi H (2020) Severe acute respiratory disease in a Huanan seafood market worker: images of an early casualty. Radiology: Cardiothoracic Imaging 2(1):1\u20132. https:\/\/doi.org\/10.1148\/ryct.2020200033","journal-title":"Radiology: Cardiothoracic Imaging"},{"key":"11913_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked 19:1\u20139. https:\/\/doi.org\/10.1016\/j.imu.2020.100360","journal-title":"Inform Med Unlocked"},{"key":"11913_CR65","doi-asserted-by":"publisher","unstructured":"Rajaraman S, Antani S (2020) Training deep learning algorithms with weakly labelled pneumonia chest X-ray data for COVID-19 detection. medRxiv preprint. https:\/\/doi.org\/10.1101\/2020.05.04.20090803","DOI":"10.1101\/2020.05.04.20090803"},{"key":"11913_CR66","doi-asserted-by":"publisher","unstructured":"Ranjan R, Sharma A, Verma MK (2021) Characterization of the second wave of COVID-19 in India, medRxiv 1-16. https:\/\/doi.org\/10.1101\/2021.04.17.21255665","DOI":"10.1101\/2021.04.17.21255665"},{"key":"11913_CR67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-65981-7_12","volume-title":"Classification in BioApps. Lecture notes in computational vision and biomechanics 26","author":"MI Razzak","year":"2018","unstructured":"Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. In: Dey N, Ashour A, Borra S (eds) Classification in BioApps. Lecture notes in computational vision and biomechanics 26. Springer Cham. https:\/\/doi.org\/10.1007\/978-3-319-65981-7_12"},{"key":"11913_CR68","doi-asserted-by":"publisher","unstructured":"Rodriguez-Morales A, Bonilla-Aldana DK, Tiwari R, Sah R, Rabaan AA, Dhama K (2020) COVID-19, an emerging coronavirus infection: current scenario and recent developments \u2013 An overview. Journal of pure and applied microbiology 14(1):6150. https:\/\/doi.org\/10.22207\/JPAM.14.1.02","DOI":"10.22207\/JPAM.14.1.02"},{"issue":"1","key":"11913_CR69","first-page":"3","volume":"28","author":"AJ Rodriguez-Morales","year":"2020","unstructured":"Rodriguez-Morales AJ, Bonilla-Aldana DK, Balbin-Ramon GJ, Rabaan AA, Sah R, Paniz-Mondolfi A, Pagliano P, Esposito S (2020) History is repeating itself: probable zoonotic spillover as the cause of the 2019 novel coronavirus epidemic. Infez Med 28(1):3\u20135","journal-title":"Infez Med"},{"key":"11913_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jaut.2020.102433","volume":"109","author":"HA Rothan","year":"2020","unstructured":"Rothan HA, Byrareddy SN (2020) The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 109:1\u20134. https:\/\/doi.org\/10.1016\/j.jaut.2020.102433","journal-title":"J Autoimmun"},{"key":"11913_CR71","unstructured":"SARS-CoV-2 Variant Classifications and Definitions (2021), Centers for Disease Control and Prevention. https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/variants\/variant-info.html.Accessed on: 03-07-2021"},{"issue":"7","key":"11913_CR72","doi-asserted-by":"publisher","first-page":"799","DOI":"10.5858\/arpa.2020-0901-SA","volume":"144","author":"DA Schwartz","year":"2020","unstructured":"Schwartz DA (2020) An analysis of 38 pregnant women with COVID-19, their newborn infants, and maternal-fetal transmission of SARS-CoV-2: maternal coronavirus infections and pregnancy outcomes. Arch Pathol Lab Med 144(7):799\u2013805. https:\/\/doi.org\/10.5858\/arpa.2020-0901-SA","journal-title":"Arch Pathol Lab Med"},{"key":"11913_CR73","doi-asserted-by":"crossref","unstructured":"Sethy PK, Behera SK, Ratha PK, Biswas P (2020) Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. MDPI AG 1-10","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"11913_CR74","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10140-020-01886-y","volume":"28","author":"V Shah","year":"2021","unstructured":"Shah V, Keniya R, Shridharani A, Punjabi M, Shah J, Mehendale N (2021) Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emerg Radiol 28:497\u2013505. https:\/\/doi.org\/10.1007\/s10140-020-01886-y","journal-title":"Emerg Radiol"},{"key":"11913_CR75","doi-asserted-by":"publisher","unstructured":"Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y (2020) Lung infection quantification of COVID-19 in CT images with deep learning. Preprint arXiv: 200304655:1-19. 48:1633\u20131645. https:\/\/doi.org\/10.1002\/mp.14609","DOI":"10.1002\/mp.14609"},{"issue":"4","key":"11913_CR76","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). Indian J Pediatrics 87(4):281\u2013286. https:\/\/doi.org\/10.1007\/s12098-020-03263-6","journal-title":"Indian J Pediatrics"},{"key":"11913_CR77","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1007\/s10489-020-02055-x","volume":"51","author":"C Sitaula","year":"2021","unstructured":"Sitaula C, Hossain MB (2021) Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Appl Intell 51:2850\u20132863. https:\/\/doi.org\/10.1007\/s10489-020-02055-x","journal-title":"Appl Intell"},{"key":"11913_CR78","unstructured":"Strengthening medical imaging (2021) https:\/\/www.who.int\/activities\/strengthening-medical-imaging. Accessed on: 09-09-2021"},{"issue":"10","key":"11913_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00140-0","volume":"9","author":"W Tan","year":"2021","unstructured":"Tan W, Liu P, Li X, Liu Y, Zhou Q, Chen C, Gong Z, Yin X, Zhang Y (2021) Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network. Health Inf Sci Syst 9(10):1\u201312. https:\/\/doi.org\/10.1007\/s13755-021-00140-0","journal-title":"Health Inf Sci Syst"},{"key":"11913_CR80","unstructured":"Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Liu J, Shen D (2020) Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. Preprint arXiv:2003.11988 1-18"},{"key":"11913_CR81","unstructured":"The Important Role Medical Imaging Plays in Diagnosis and Treatment, Peconic Bay Medical Center (2018). https:\/\/www.pbmchealth.org\/news-events\/blog\/important-role-medical-imaging-plays-diagnosis-and-treatment. Accessed on: 09-09-2021"},{"key":"11913_CR82","unstructured":"Tracking SARS-CoV-2 variants (2021). https:\/\/www.who.int\/en\/activities\/tracking-SARS-CoV-2-variants\/. Accessed on: 03-07-2021"},{"key":"11913_CR83","unstructured":"Treatments for COVID-19 (2021) Harvard Health Publishing, Harvard Medical School. https:\/\/www.health.harvard.edu\/diseases-and-conditions\/treatments-for-covid-19. Accessed on 03-07-2021"},{"issue":"2","key":"11913_CR84","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cell.2020.02.058","volume":"181","author":"AC Walls","year":"2020","unstructured":"Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D (2020) Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 181(2):281\u2013292. https:\/\/doi.org\/10.1016\/j.cell.2020.02.058","journal-title":"Cell"},{"issue":"7","key":"11913_CR85","doi-asserted-by":"publisher","first-page":"e00127","DOI":"10.1128\/JVI.00127-20","volume":"94","author":"Y Wan","year":"2020","unstructured":"Wan Y, Shang J, Graham R, Baric RS, Li F (2020) Receptor recognition by the novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS coronavirus. J Virol 94(7):e00127\u2013e00120. https:\/\/doi.org\/10.1128\/JVI.00127-20","journal-title":"J Virol"},{"issue":"10223","key":"11913_CR86","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/S0140-6736(20)30185-9","volume":"395","author":"C Wang","year":"2020","unstructured":"Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):470\u2013473. https:\/\/doi.org\/10.1016\/S0140-6736(20)30185-9","journal-title":"Lancet"},{"issue":"2","key":"11913_CR87","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1148\/radiol.2020200843","volume":"296","author":"Y Wang","year":"2020","unstructured":"Wang Y, Dong C, Hu Y, Li C, Ren Q, Zhang X, Shi H, Zhou M (2020) Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal study. Radiology 296(2):55\u201364. https:\/\/doi.org\/10.1148\/radiol.2020200843","journal-title":"Radiology"},{"key":"11913_CR88","doi-asserted-by":"publisher","unstructured":"Wang L, Shi Y, Xiao T, Fu J, Feng X, Mu D, Feng Q, Hei M, Hu X, Li Z, Lu G, Tang Z, Wang Y, Wang C, Xia S, Xu J, Yang Y, Yang J, Zeng M, \u2026 Zhou W (2020) Working Committee on Perinatal and Neonatal Management for the Prevention and Control of the 2019 Novel Coronavirus Infection, Chinese expert consensus on the perinatal and neonatal management for the prevention and control of the 2019 novel coronavirus infection (First edition). Ann Transl Med. 8(3):47. https:\/\/doi.org\/10.21037\/atm.2020.02.20","DOI":"10.21037\/atm.2020.02.20"},{"issue":"19549","key":"11913_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. Nature research. Sci Rep 10(19549):1\u201312. https:\/\/doi.org\/10.1038\/s41598-020-76550-z","journal-title":"Sci Rep"},{"key":"11913_CR90","doi-asserted-by":"publisher","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","volume":"31","author":"S Wang","year":"2021","unstructured":"Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2021) A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 31:6096\u20136104. https:\/\/doi.org\/10.1007\/s00330-021-07715-1","journal-title":"Eur Radiol"},{"issue":"6483","key":"11913_CR91","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1126\/science.abb2507","volume":"367","author":"D Wrapp","year":"2020","unstructured":"Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh CL, Abiona O, Graham BS, McLellan JS (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483):1260\u20131263. https:\/\/doi.org\/10.1126\/science.abb2507","journal-title":"Science"},{"key":"11913_CR92","doi-asserted-by":"publisher","unstructured":"Xiao K, Zhai J, Feng Y, Zhou N, Zhang X, Zou JJ, Li N, Guo Y, Li X, Shen X, Zhang Z, Shu F, Huang W, Li Y, Zhang Z, Chen RA, Wu YJ, Peng SM, Huang M, Xie WJ, Cai QH, Hou FH, Liu Y, Chen W, Xiao L, Shen Y (2020) Isolation and characterization of 2019-nCoV-like coronavirus from Malayan pangolins. bioRxiv 1-31. https:\/\/doi.org\/10.1101\/2020.02.17.951335","DOI":"10.1101\/2020.02.17.951335"},{"issue":"4","key":"11913_CR93","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":"10","key":"11913_CR94","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2020","unstructured":"Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Chen Y, Su J, Lang G, Li Y, Zhao H, Xu K, Ruan L, Wu W (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10):1122\u20131129. https:\/\/doi.org\/10.1016\/j.eng.2020.04.010","journal-title":"Engineering"},{"key":"11913_CR95","doi-asserted-by":"publisher","unstructured":"Young BE, Ong SWX, Kalimuddin S, Low JG, Tan SY, Loh J, Ng OT, Marimuthu K, Ang LW, Mak TM, Lau SK, Anderson DE, Chan KS, Tan TY, Ng TY, Cui L, Said Z, Kurupatham L, Cheng MI, \u2026 Lye DC (2020) Epidemiologic features and clinical course of patients infected with SARS-CoV-2 in Singapore. JAMA 323(15):1488\u20131494. https:\/\/doi.org\/10.1001\/jama.2020.3204","DOI":"10.1001\/jama.2020.3204"},{"key":"11913_CR96","doi-asserted-by":"publisher","unstructured":"Yu L, Shanshan Wu, Xiaowen Hao, Xuelong Li, Xiyang Liu, Shenglong Ye, Han H, Dong X, Li X, Li J, Liu J, Liu N, Zhang W, Pelechano V, Chen W, Yin X (2020) Rapid colorimetric detection of COVID-19 coronavirus using a reverse transcriptional loop-mediated isothermal amplification (RT-LAMP) diagnostic platform: iLACO. Clinical Chemistry 1\u201319. https:\/\/doi.org\/10.1093\/clinchem\/hvaa102","DOI":"10.1093\/clinchem\/hvaa102"},{"issue":"4","key":"11913_CR97","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1002\/jmv.25674","volume":"92","author":"N Zhang","year":"2020","unstructured":"Zhang N, Wang L, Deng X, Liang R, Su M, He C, Hu L, Su Y, Ren J, Yu F, Du L, Jiang S (2020) Recent advances in the detection of respiratory virus infection in humans. J Med Virol 92(4):408\u2013417. https:\/\/doi.org\/10.1002\/jmv.25674","journal-title":"J Med Virol"},{"key":"11913_CR98","unstructured":"Zhang J, Xie Y, Li Y, Shen C, Xia Y (2020) COVID-19 screening on chest X-ray images using deep learning based Anomaly Detection. arXiv preprint arXiv:2003.12338v1:1\u20136"},{"key":"11913_CR99","doi-asserted-by":"crossref","unstructured":"Zhao D, Yao F, Wang L, Zheng L, Gao Y, Ye J, Guo F, Zhao H, Gao R (2020) Comparative study on the clinical features of COVID-19 pneumonia to other pneumonias. Oxford academic, clinical infectious diseases, article | WHO COVID | ID: covidwho-7938, 2020","DOI":"10.1093\/cid\/ciaa247"},{"key":"11913_CR100","doi-asserted-by":"publisher","unstructured":"Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL, Chen HD, Chen J, Luo Y, Guo H, Jiang RD, Liu MQ, Chen Y, Shen XR, Wang X, \u2026 Shi ZL (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270\u2013273. https:\/\/doi.org\/10.1038\/s41586-020-2012-7","DOI":"10.1038\/s41586-020-2012-7"},{"issue":"2","key":"11913_CR101","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1148\/radiol.2020200490","volume":"296","author":"ZY Zu","year":"2020","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology 296(2):15\u201325. https:\/\/doi.org\/10.1148\/radiol.2020200490","journal-title":"Radiology"},{"issue":"2","key":"11913_CR102","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1148\/radiol.2020200490","volume":"296","author":"ZY Zu","year":"2020","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology 296(2):15\u201325. https:\/\/doi.org\/10.1148\/radiol.2020200490","journal-title":"Radiology"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11913-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-11913-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11913-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T06:33:55Z","timestamp":1653546835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-11913-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,15]]},"references-count":102,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["11913"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-11913-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,15]]},"assertion":[{"value":"3 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}