{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:30:21Z","timestamp":1776472221179,"version":"3.51.2"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"35-36","license":[{"start":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T00:00:00Z","timestamp":1603670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T00:00:00Z","timestamp":1603670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s00521-020-05437-x","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T06:02:45Z","timestamp":1603692165000},"page":"29099-29111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":153,"title":["A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-4566","authenticated-orcid":false,"given":"Mohamed","family":"Loey","sequence":"first","affiliation":[]},{"given":"Gunasekaran","family":"Manogaran","sequence":"additional","affiliation":[]},{"given":"Nour Eldeen M.","family":"Khalifa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"5437_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2020.02.003","author":"L Chang","year":"2020","unstructured":"Chang L, Yan Y, Wang L (2020) Coronavirus disease 2019: coronaviruses and blood safety. Transfus Med Rev. https:\/\/doi.org\/10.1016\/j.tmrv.2020.02.003","journal-title":"Transfus Med Rev"},{"issue":"4","key":"5437_CR2","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 Pediatr 87(4):281\u2013286. https:\/\/doi.org\/10.1007\/s12098-020-03263-6","journal-title":"Indian J Pediatr"},{"issue":"3","key":"5437_CR3","doi-asserted-by":"publisher","first-page":"105924","DOI":"10.1016\/j.ijantimicag.2020.105924","volume":"55","author":"C-C Lai","year":"2020","unstructured":"Lai C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R (2020) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents 55(3):105924. https:\/\/doi.org\/10.1016\/j.ijantimicag.2020.105924","journal-title":"Int J Antimicrob Agents"},{"issue":"3","key":"5437_CR4","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S1473-3099(20)30063-3","volume":"20","author":"J Li","year":"2020","unstructured":"Li J et al (2020) Game consumption and the 2019 novel coronavirus. Lancet Infect Dis 20(3):275\u2013276. https:\/\/doi.org\/10.1016\/S1473-3099(20)30063-3","journal-title":"Lancet Infect Dis"},{"key":"5437_CR5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.3864","author":"JM Sharfstein","year":"2020","unstructured":"Sharfstein JM, Becker SJ, Mello MM (2020) Diagnostic testing for the novel coronavirus. JAMA. https:\/\/doi.org\/10.1001\/jama.2020.3864","journal-title":"JAMA"},{"key":"5437_CR6","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.jare.2020.03.005","volume":"24","author":"MA Shereen","year":"2020","unstructured":"Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R (2020) COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J Adv Res 24:91\u201398. https:\/\/doi.org\/10.1016\/j.jare.2020.03.005","journal-title":"J Adv Res"},{"issue":"3","key":"5437_CR7","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/pathogens9030231","volume":"9","author":"FA Rabi","year":"2020","unstructured":"Rabi FA, Al Zoubi MS, Kasasbeh GA, Salameh DM, Al-Nasser AD (2020) SARS-CoV-2 and coronavirus disease 2019: what we know so far. Pathogens 9(3):231. https:\/\/doi.org\/10.3390\/pathogens9030231","journal-title":"Pathogens"},{"issue":"4","key":"5437_CR8","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/s41579-020-0336-9","volume":"18","author":"A York","year":"2020","unstructured":"York A (2020) Novel coronavirus takes flight from bats? Nat Rev Microbiol 18(4):191. https:\/\/doi.org\/10.1038\/s41579-020-0336-9","journal-title":"Nat Rev Microbiol"},{"key":"5437_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2169-0","author":"TT-Y Lam","year":"2020","unstructured":"Lam TT-Y et al (2020) Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins. Nature. https:\/\/doi.org\/10.1038\/s41586-020-2169-0","journal-title":"Nature"},{"issue":"5","key":"5437_CR10","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1002\/jmv.25699","volume":"92","author":"M Giovanetti","year":"2020","unstructured":"Giovanetti M, Benvenuto D, Angeletti S, Ciccozzi M (2020) The first two cases of 2019-nCoV in Italy: where they come from? J Med Virol 92(5):518\u2013521. https:\/\/doi.org\/10.1002\/jmv.25699","journal-title":"J Med Virol"},{"issue":"10","key":"5437_CR11","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1056\/NEJMoa2001191","volume":"382","author":"ML Holshue","year":"2020","unstructured":"Holshue ML et al (2020) First case of 2019 novel coronavirus in the United States. N Engl J Med 382(10):929\u2013936. https:\/\/doi.org\/10.1056\/NEJMoa2001191","journal-title":"N Engl J Med"},{"issue":"10","key":"5437_CR12","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1056\/NEJMc2001468","volume":"382","author":"C Rothe","year":"2020","unstructured":"Rothe C et al (2020) Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med 382(10):970\u2013971. https:\/\/doi.org\/10.1056\/NEJMc2001468","journal-title":"N Engl J Med"},{"issue":"9","key":"5437_CR13","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1056\/NEJMc2001272","volume":"382","author":"LT Phan","year":"2020","unstructured":"Phan LT et al (2020) Importation and human-to-human transmission of a novel coronavirus in Vietnam. N Engl J Med 382(9):872\u2013874. https:\/\/doi.org\/10.1056\/NEJMc2001272","journal-title":"N Engl J Med"},{"issue":"3","key":"5437_CR14","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/S1473-3099(20)30067-0","volume":"20","author":"A Bastola","year":"2020","unstructured":"Bastola A et al (2020) The first 2019 novel coronavirus case in Nepal. Lancet Infect Dis 20(3):279\u2013280. https:\/\/doi.org\/10.1016\/S1473-3099(20)30067-0","journal-title":"Lancet Infect Dis"},{"key":"5437_CR15","unstructured":"Coronavirus (COVID-19) map. https:\/\/www.google.com\/covid19-map\/. Accessed 26 Apr 2020"},{"key":"5437_CR16","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/j.compag.2019.05.019","volume":"162","author":"D Rong","year":"2019","unstructured":"Rong D, Xie L, Ying Y (2019) Computer vision detection of foreign objects in walnuts using deep learning. Comput Electron Agric 162:1001\u20131010. https:\/\/doi.org\/10.1016\/j.compag.2019.05.019","journal-title":"Comput Electron Agric"},{"key":"5437_CR17","doi-asserted-by":"crossref","unstructured":"Loey M, ElSawy A, Afify M (2020) Deep learning in plant diseases detection for agricultural crops: a survey. In: International journal of service science, management, engineering, and technology (IJSSMET). www.igi-global.com\/article\/deep-learning-in-plant-diseases-detection-for-agricultural-crops\/248499. Accessed 11 Apr 2020","DOI":"10.4018\/IJSSMET.2020040103"},{"issue":"2","key":"5437_CR18","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z f\u00fcr Med Phys 29(2):102\u2013127. https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","journal-title":"Z f\u00fcr Med Phys"},{"issue":"2","key":"5437_CR19","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.zemedi.2018.12.003","volume":"29","author":"A Maier","year":"2019","unstructured":"Maier A, Syben C, Lasser T, Riess C (2019) A gentle introduction to deep learning in medical image processing. Z f\u00fcr Med Phys 29(2):86\u2013101. https:\/\/doi.org\/10.1016\/j.zemedi.2018.12.003","journal-title":"Z f\u00fcr Med Phys"},{"key":"5437_CR20","doi-asserted-by":"crossref","unstructured":"Loey M, Naman MR, Zayed HH (2020) A survey on blood image diseases detection using deep learning. In: International journal of service science, management, engineering, and technology (IJSSMET). www.igi-global.com\/article\/a-survey-on-blood-image-diseases-detection-using-deep-learning\/256653. Accessed 17 Jun 2020","DOI":"10.4018\/IJSSMET.2020070102"},{"key":"5437_CR21","doi-asserted-by":"publisher","unstructured":"Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3642\u20133649. https:\/\/doi.org\/10.1109\/CVPR.2012.6248110","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"5437_CR22","unstructured":"Krizhevsky Ilya GE, Alex H (2012) Sutskever, Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"11","key":"5437_CR23","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"5437_CR24","doi-asserted-by":"publisher","unstructured":"Yin F, Wang Q, Zhang X, Liu C (2013) ICDAR 2013 Chinese handwriting recognition competition. In: 2013 12th international conference on document analysis and recognition, pp 1464\u20131470. https:\/\/doi.org\/10.1109\/icdar.2013.218","DOI":"10.1109\/icdar.2013.218"},{"key":"5437_CR25","doi-asserted-by":"publisher","unstructured":"LeCun Y, Huang FJ, Bottou L (2004) Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004., vol 2, p II-104. https:\/\/doi.org\/10.1109\/cvpr.2004.1315150","DOI":"10.1109\/cvpr.2004.1315150"},{"key":"5437_CR26","doi-asserted-by":"publisher","unstructured":"Stallkamp J, Schlipsing M, Salmen J, Igel C (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 international joint conference on neural networks, pp 1453\u20131460. https:\/\/doi.org\/10.1109\/IJCNN.2011.6033395","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"5437_CR27","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L, Kai L, Li F-F (209) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5437_CR28","doi-asserted-by":"crossref","unstructured":"El-Sawy A, EL-Bakry H, Loey M (2017) CNN for handwritten arabic digits recognition based on LeNet-5 BT. In: Proceedings of the international conference on advanced intelligent systems and informatics 2016, pp 566\u2013575","DOI":"10.1007\/978-3-319-48308-5_54"},{"key":"5437_CR29","unstructured":"El-Sawy A, Loey M, EL-Bakry H (2017) Arabic handwritten characters recognition using convolutional neural network. WSEAS Trans Comp Res 5:11\u201319. http:\/\/www.wseas.org\/multimedia\/journals\/computerresearch\/2017\/a045818-075.php. Accessed 1 Apr 2020"},{"key":"5437_CR30","doi-asserted-by":"publisher","unstructured":"Liu S, Deng W (2015) Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR), pp 730\u2013734. https:\/\/doi.org\/10.1109\/acpr.2015.7486599","DOI":"10.1109\/acpr.2015.7486599"},{"key":"5437_CR31","doi-asserted-by":"publisher","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5437_CR32","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"5437_CR33","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1800\u20131807, https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"5437_CR34","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2261\u20132269. https:\/\/doi.org\/10.1109\/cvpr.2017.243","DOI":"10.1109\/cvpr.2017.243"},{"key":"5437_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"5437_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/sym12040651","author":"M Loey","year":"2020","unstructured":"Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry. https:\/\/doi.org\/10.3390\/sym12040651","journal-title":"Symmetry"},{"issue":"10","key":"5437_CR37","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1097\/RLI.0000000000000574","volume":"54","author":"A Christe","year":"2019","unstructured":"Christe A et al (2019) Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol 54(10):627\u2013632. https:\/\/doi.org\/10.1097\/RLI.0000000000000574","journal-title":"Invest Radiol"},{"key":"5437_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56989-5","author":"D Bermejo-Pel\u00e1ez","year":"2020","unstructured":"Bermejo-Pel\u00e1ez D, Ash SY, Washko GR, San Jos\u00e9 Est\u00e9par R, Ledesma-Carbayo MJ (2020) Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-019-56989-5","journal-title":"Sci Rep"},{"key":"5437_CR39","unstructured":"Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-Dataset: a CT scan dataset about COVID-19. arXiv:2003.13865 [cs, eess, stat] http:\/\/arxiv.org\/abs\/2003.13865. Accessed 9 Apr 2020"},{"key":"5437_CR40","doi-asserted-by":"publisher","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 international interdisciplinary PhD workshop (IIPhDW), pp. 117\u2013122. https:\/\/doi.org\/10.1109\/iiphdw.2018.8388338","DOI":"10.1109\/iiphdw.2018.8388338"},{"key":"5437_CR41","unstructured":"Kingma DP (2015) Adam: a method for stochastic optimization. http:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"56","key":"5437_CR42","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(56):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"5437_CR43","unstructured":"Caruana R, Lawrence S, Giles L (2000) Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Proceedings of the 13th international conference on neural information processing systems, pp 381\u2013387"},{"key":"5437_CR44","unstructured":"Goutte C, Gaussier E (2010) A probabilistic interpretation of Precision, Recall and F-Score, with implication for evaluation"},{"key":"5437_CR45","unstructured":"Smarandache F (2019) Neutrosophic set is a generalization of intuitionistic fuzzy set, inconsistent intuitionistic fuzzy set (Picture Fuzzy Set, Ternary Fuzzy Set), Pythagorean Fuzzy Set, q-Rung Orthopair Fuzzy Set, Spherical Fuzzy Set, etc. arXiv:1911.07333 [math] http:\/\/arxiv.org\/abs\/1911.07333. Accessed 13 Apr 2020"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05437-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05437-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05437-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T11:48:21Z","timestamp":1766058501000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05437-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,26]]},"references-count":45,"journal-issue":{"issue":"35-36","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["5437"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05437-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,26]]},"assertion":[{"value":"2 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2020","order":3,"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":"The author declares no conflict of interest.","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"}]}}