{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:18:06Z","timestamp":1768681086556,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"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":[[2022,9]]},"DOI":"10.1007\/s11042-022-12640-6","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T19:26:15Z","timestamp":1649445975000},"page":"31201-31218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19"],"prefix":"10.1007","volume":"81","author":[{"given":"Saloni","family":"Laddha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3460-6989","authenticated-orcid":false,"given":"Vijay","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"12640_CR1","doi-asserted-by":"publisher","first-page":"17589","DOI":"10.1007\/s00521-021-06344-5","volume":"33","author":"E Acar","year":"2021","unstructured":"Acar E, Sahin E, Yilmaz I (2021) Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. Neural Comput & Applic 33:17589\u201317609","journal-title":"Neural Comput & Applic"},{"key":"12640_CR2","doi-asserted-by":"publisher","first-page":"7174","DOI":"10.3390\/app11167174","volume":"11","author":"AA Al-Shargabi","year":"2021","unstructured":"Al-Shargabi AA, Alshobaili JF, Alabdulatif A, Alrobah N (2021) COVID-CGAN: efficient deep learning approach for COVID-19 detection based on CXR images using conditional GANs. Appl Sci 11:7174","journal-title":"Appl Sci"},{"key":"12640_CR3","unstructured":"Apostolopoulos ID, Bessiana T, \u201cCOVID-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks\u201d, arXiv:2003.11617"},{"key":"12640_CR4","unstructured":"Beers A, Brown JM, Chang K, Campbell JP, Ostmo S, Chiang MF, and Kalpathy-Cramer J (2018) \u201cHigh-resolution medical image synthesis using progressively grown generative adversarial networks,\u201d ArXiv, vol. abs\/1805.03144,"},{"key":"12640_CR5","doi-asserted-by":"crossref","unstructured":"Chen H, Cao P Deep learning-based data augmentation and classification for limited medical data learning. In: 2019 IEEE international conference on power, intelligent computing and systems (ICPICS) 2019 Jul 12. IEEE, pp 300\u2013303","DOI":"10.1109\/ICPICS47731.2019.8942411"},{"key":"12640_CR6","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2020.03.120","volume":"428","author":"X Chen","year":"2021","unstructured":"Chen X, Xu J, Zhou R, Chen W, Fang J, Liu C (2021) TrajVAE: a Variational AutoEncoder model for trajectory generation. Neurocomputing. 428:332\u2013339","journal-title":"Neurocomputing."},{"key":"12640_CR7","unstructured":"Dataset_4_classes, Transfer-Learning-COVID-19. [Accessed: Juley 24, 2021], [Available Online]: https:\/\/github.com\/vj2050\/Transfer-Learning-COVID-19"},{"key":"12640_CR8","unstructured":"de la Iglesia Vay\u00e1 M, Saborit JM, Montell JA, Pertusa A, Bustos A, Cazorla M, Galant J, Barber X, Orozco-Beltr\u00e1n D, Garcia F, et al. (2020) Bimcv COVID-19+: a large annotated dataset of rx and ct images from COVID-19 patients. arXiv preprint arXiv:2006.01174"},{"key":"12640_CR9","doi-asserted-by":"crossref","unstructured":"DeGrave AJ, Janizek JD, and Lee S-I (2020) Ai for radiographic COVID-19 detection selects shortcuts over the signal. medRxiv,","DOI":"10.1101\/2020.09.13.20193565"},{"issue":"4","key":"12640_CR10","doi-asserted-by":"publisher","first-page":"3197","DOI":"10.1109\/TAES.2020.2969579","volume":"56","author":"B Erol","year":"2020","unstructured":"Erol B, Gurbuz SZ, Amin MG (2020) Motion classification using kinematically sifted acgan-synthesized radar micro-doppler signatures. IEEE Trans Aerosp Electron Syst 56(4):3197\u20133213","journal-title":"IEEE Trans Aerosp Electron Syst"},{"key":"12640_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 321:321\u2013331","journal-title":"Neurocomputing."},{"key":"12640_CR12","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1109\/ICIEA.2019.8833686","volume-title":"2019 14th IEEE conference on industrial electronics and applications (ICIEA)","author":"L Gonog","year":"2019","unstructured":"Gonog L, Zhou Y (2019) A review: generative adversarial networks. In: 2019 14th IEEE conference on industrial electronics and applications (ICIEA). IEEE, pp 505\u2013510"},{"key":"12640_CR13","unstructured":"Hemdan EED, Shouman MA, Karar ME (2020) COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. 2020 arXiv preprint arXiv:2003.11055"},{"issue":"1","key":"12640_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-87994-2","volume":"11","author":"S Motamed","year":"2021","unstructured":"Motamed S, Rogalla P, Khalvati F (2021) RANDGAN: randomized generative adversarial network for detection of COVID-19 in chest X-ray. Sci Rep 11(1):1","journal-title":"Sci Rep"},{"key":"12640_CR15","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z (2020) \u201cAutomatic Detection of Coronavirus Disease (COVID-19) Using X-Ray Images and Deep Convolutional Neural Networks\u201d arXiv preprint arXiv:2003.10849","DOI":"10.1007\/s10044-021-00984-y"},{"issue":"1","key":"12640_CR16","first-page":"e200034","volume":"2","author":"M-Y Ng","year":"2020","unstructured":"Ng M-Y, Lee EYP, Yang J, Yang F, Li X, Wang H, Lui MM-S, Lo CS-Y, Leung B, Khong P-L et al (2020) Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol: Cardiothoracic Imag 2(1):e200034","journal-title":"Radiol: Cardiothoracic Imag"},{"key":"12640_CR17","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv, 2016; arXiv:1511.06434"},{"key":"12640_CR18","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.media.2019.06.014","volume":"57","author":"M Rubin","year":"2019","unstructured":"Rubin M, Stein O, Turko NA, Nygate Y, Roitshtain D, Karako L, Barnea I, Giryes R, Shaked NT (2019) TOP-GAN: stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 57:176\u2013185","journal-title":"Med Image Anal"},{"key":"12640_CR19","doi-asserted-by":"crossref","unstructured":"Schlegl T, Seeb\u00f6ck P, Waldstein SM, Schmidt-Erfurth U, and Langs G (2017) \u201cUnsupervised anomaly detection with generative adversarial networks to guide marker discovery,\u201d in IPMI","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"12640_CR20","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202003.0300.v1","volume-title":"Detection of coronavirus disease (COVID-19) based on deep features","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Behera SK (2020) Detection of coronavirus disease (COVID-19) based on deep features"},{"key":"12640_CR21","first-page":"2020","volume":"6","author":"A Sharma","year":"2020","unstructured":"Sharma A, Rani S, Gupta D (2020) Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases. Int J Biomed Imaging 6:2020","journal-title":"Int J Biomed Imaging"},{"key":"12640_CR22","doi-asserted-by":"publisher","first-page":"105611","DOI":"10.1016\/j.cmpb.2020.105611","volume":"196","author":"G Shi","year":"2020","unstructured":"Shi G, Wang J, Qiang Y, Yang X, Zhao J, Hao R, Yang W, Du Q, Kazihise NG (2020) Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Comput Methods Prog Biomed 196:105611","journal-title":"Comput Methods Prog Biomed"},{"key":"12640_CR23","doi-asserted-by":"publisher","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","volume":"8","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) COVIDgan: data augmentation using auxiliary classifier Gan for improved COVID-19 detection. Ieee Access 8:91916\u201391923","journal-title":"Ieee Access"},{"key":"12640_CR24","doi-asserted-by":"publisher","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","volume":"8","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8:91916\u201391923","journal-title":"IEEE Access"},{"key":"12640_CR25","doi-asserted-by":"crossref","unstructured":"Wang L, Wong A (2020) \u201cCOVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images\u201d. arXiv preprint arXiv:2003.09871.","DOI":"10.1038\/s41598-020-76550-z"},{"key":"12640_CR26","doi-asserted-by":"crossref","unstructured":"Wang L and Wong Alexander (2020) COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. arXiv preprint arXiv:2003.09871","DOI":"10.1038\/s41598-020-76550-z"},{"key":"12640_CR27","doi-asserted-by":"publisher","first-page":"519","DOI":"10.3390\/sym10100519","volume":"10","author":"D Zhao","year":"2018","unstructured":"Zhao D, Zhu D, Lu J, Luo Y, Zhang G (2018) Synthetic medical images using F&BGAN for improved lung nodules classification by MultiScale VGG16. Symmetry 10:519","journal-title":"Symmetry"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12640-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T05:09:59Z","timestamp":1661144999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12640-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":27,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["12640"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12640-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]},"assertion":[{"value":"11 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 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 declared that no conflict of interests or competing interests exist.","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"}]}}