{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:23Z","timestamp":1771459223305,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"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":["New Gener. Comput."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00354-022-00172-4","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T12:20:22Z","timestamp":1651839622000},"page":"1077-1091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1866-4721","authenticated-orcid":false,"given":"Muhammed","family":"Yildirim","sequence":"first","affiliation":[]},{"given":"Orkun","family":"Ero\u011flu","sequence":"additional","affiliation":[]},{"given":"Ye\u015fim","family":"Ero\u011flu","sequence":"additional","affiliation":[]},{"given":"Ahmet","family":"\u00c7inar","sequence":"additional","affiliation":[]},{"given":"Emine","family":"Cengil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"172_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaut.2020.102433","volume":"109","author":"HA Rothan","year":"2020","unstructured":"Rothan, H.A., Byrareddy, S.N.: The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 109, 102433 (2020)","journal-title":"J. Autoimmun."},{"issue":"5","key":"172_CR2","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s00259-020-04735-9","volume":"47","author":"X Xu","year":"2020","unstructured":"Xu, X., et al.: Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur. J. Nucl. Med. Mol. Imaging 47(5), 1275\u20131280 (2020). https:\/\/doi.org\/10.1007\/s00259-020-04735-9","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"172_CR3","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m800","volume":"368","author":"MS Razai","year":"2020","unstructured":"Razai, M.S.: Coronavirus disease 2019 (covid-19): a guide for UK GPs. Bmj 368:m800 (2020)","journal-title":"Bmj"},{"issue":"1142","key":"172_CR4","first-page":"753","volume":"96","author":"S Umakanthan","year":"2020","unstructured":"Umakanthan, S., et al.: Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgrad. Med. J. 96(1142), 753\u2013758 (2020)","journal-title":"Postgrad. Med. J."},{"key":"172_CR5","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1148\/radiol.2020200370","volume":"295","author":"F Pan","year":"2020","unstructured":"Pan, F., et al.: Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology 295, 715\u2013721 (2020)","journal-title":"Radiology"},{"key":"172_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.clinimag.2020.04.001","volume":"64","author":"A Jacobi","year":"2020","unstructured":"Jacobi, A., et al.: Cardiothoracic imaging portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clinical Imaging 64, 35\u201342 (2020)","journal-title":"Clinical Imaging"},{"issue":"2","key":"172_CR7","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/ima.22552","volume":"31","author":"V Jahmunah","year":"2021","unstructured":"Jahmunah, V., et al.: Future IoT tools for COVID-19 contact tracing and prediction: a review of the state-of-the-science. Int. J. Imaging Syst. Technol. 31(2), 455\u2013471 (2021)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"172_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113564","volume":"159","author":"D Moitra","year":"2020","unstructured":"Moitra, D., Mandal, R.K.: Classification of non-small cell lung cancer using one-dimensional convolutional neural network. Expert Syst. Appl. 159, 113564 (2020)","journal-title":"Expert Syst. Appl."},{"key":"172_CR9","first-page":"117340E","volume":"11734","author":"HS Maghdid","year":"2021","unstructured":"Maghdid, H.S., et al.: (2021) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal Image Exploitation Learning 11734, 117340E (2021). (International Society for Optics and Photonics)","journal-title":"In Multimodal Image Exploitation Learning"},{"issue":"3","key":"172_CR10","doi-asserted-by":"publisher","first-page":"461","DOI":"10.18280\/ts.370313","volume":"37","author":"M Yildirim","year":"2020","unstructured":"Yildirim, M., Cinar, A.C.: A deep learning based hybrid approach for COVID-19 disease detections. Traitement du Signal 37(3), 461\u2013468 (2020)","journal-title":"Traitement du Signal"},{"key":"172_CR11","first-page":"1","volume":"39","author":"K El Asnaoui","year":"2020","unstructured":"El Asnaoui, K., Chawki, Y.: Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 39, 1\u201312 (2020)","journal-title":"J Biomol Struct Dyn"},{"issue":"1","key":"172_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-42294-8","volume":"9","author":"IM Baltruschat","year":"2019","unstructured":"Baltruschat, I.M., et al.: Comparison of deep learning approaches for multi-label chest X-ray classification. Sci. Rep. 9(1), 1\u201310 (2019)","journal-title":"Sci. Rep."},{"key":"172_CR13","unstructured":"Farid, A.A.: A CNN Classification Model For Diagnosis Covid19 (2020)"},{"key":"172_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan, M.F., et al.: CNN-based transfer learning\u2013BiLSTM network: a novel approach for COVID-19 infection detection. Appl. Soft Comput. 98, 106912 (2021)","journal-title":"Appl. Soft Comput."},{"key":"172_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan, A.I., Shah, J.L., Bhat, M.M.: CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Programs Biomed. 196, 105581 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"key":"172_CR16","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3557984","author":"X Bai","year":"2020","unstructured":"Bai, X., et al.: Predicting COVID-19 malignant progression with AI techniques. SSRN J (2020). https:\/\/doi.org\/10.2139\/ssrn.3557984. (Preprint posted online on March, 2020, 31)","journal-title":"SSRN J"},{"key":"172_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103869","volume":"122","author":"T Mahmud","year":"2020","unstructured":"Mahmud, T., Rahman, M.A., Fattah, S.A.: CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput. Biol. Med. 122, 103869 (2020)","journal-title":"Comput. Biol. Med."},{"key":"172_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar, F., Korkmaz, D.: COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med. Hypotheses 140, 109761 (2020)","journal-title":"Med. Hypotheses"},{"key":"172_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794","volume":"65","author":"S Minaee","year":"2020","unstructured":"Minaee, S., et al.: Deep-covid: predicting covid-19 from chest x-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020)","journal-title":"Med. Image Anal."},{"issue":"15","key":"172_CR20","doi-asserted-by":"publisher","first-page":"8052","DOI":"10.3390\/ijerph18158052","volume":"18","author":"PD Barua","year":"2021","unstructured":"Barua, P.D., et al.: Automatic COVID-19 detection using exemplar hybrid deep features with X-ray images. Int. J. Environ. Res. Public Health 18(15), 8052 (2021)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"172_CR21","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"ME Chowdhury","year":"2020","unstructured":"Chowdhury, M.E., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665\u2013132676 (2020)","journal-title":"IEEE Access"},{"key":"172_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104319","volume":"132","author":"T Rahman","year":"2021","unstructured":"Rahman, T., et al.: Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput. Biol. Med. 132, 104319 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"172_CR23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.18280\/ts.380117","volume":"38","author":"A \u00c7\u0131nar","year":"2021","unstructured":"\u00c7\u0131nar, A., Y\u0131ld\u0131r\u0131m, M., Ero\u011flu, Y.: Classification of pneumonia cell images using improved ResNet50 model. Traitement du Signal 38(1), 165\u2013173 (2021)","journal-title":"Traitement du Signal"},{"key":"172_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22632","author":"Y Eroglu","year":"2021","unstructured":"Eroglu, Y., Yildirim, M., Cinar, A..: mRMR\u2010based hybrid convolutional neural network model for classification of Alzheimer's disease on brain magnetic resonance images. Int. J. Imaging Syst. Technol. 32(2), 517\u2212527 (2022)","journal-title":"Int J Imaging Syst Technol"},{"key":"172_CR25","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22623","author":"M Yildirim","year":"2022","unstructured":"Yildirim, M., Cinar, A.: Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. Int. J. Imaging Syst. Technol. 32(1), 155\u2212162 (2022)","journal-title":"Int J Imaging Syst Technol"},{"key":"172_CR26","doi-asserted-by":"crossref","unstructured":"ZHANG, T., ZHANG, X., SHI, J., WEI, S.: High-speed ship detection in SAR images by improved yolov3. In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing pp. 149\u2212152. IEEE.(2019, December)","DOI":"10.1109\/ICCWAMTIP47768.2019.9067695"},{"key":"172_CR27","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. Preprint arXiv:1704.04861 (2017)"},{"key":"172_CR28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A.,Chen, L. C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 4510\u22124520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"172_CR29","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning pp. 6105\u22126114. PMLR. (2019, May)"},{"key":"172_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108151","volume":"166","author":"S Dogan","year":"2020","unstructured":"Dogan, S., Akbal, E., Tuncer, T.: A novel ternary and signum kernelled linear hexadecimal pattern and hybrid feature selection based environmental sound classification method. Measurement 166, 108151 (2020)","journal-title":"Measurement"},{"key":"172_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104407","volume":"133","author":"Y Ero\u011flu","year":"2021","unstructured":"Ero\u011flu, Y., Yildirim, M., \u00c7inar, A.: Convolutional neural networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Comput. Biol. Med. 133, 104407 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"172_CR32","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.3390\/rs12081257","volume":"12","author":"ME Paoletti","year":"2020","unstructured":"Paoletti, M.E., et al.: A new GPU implementation of support vector machines for fast hyperspectral image classification. Remote Sensing 12(8), 1257 (2020)","journal-title":"Remote Sensing"},{"key":"172_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106010","volume":"203","author":"JEW Koh","year":"2021","unstructured":"Koh, J.E.W., et al.: Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. Comput. Methods Programs Biomed. 203, 106010 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"172_CR34","doi-asserted-by":"crossref","unstructured":"Cengil, E., Cinar, A.: A deep learning based approach to lung cancer identification. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) pp. 1\u22125. IEEE. (2018, September)","DOI":"10.1109\/IDAP.2018.8620723"},{"issue":"6","key":"172_CR35","first-page":"651","volume":"9","author":"M Yildirim","year":"2021","unstructured":"Yildirim, M., \u00c7inar, A.: A new model for classification of human movements on videos using convolutional neural networks: MA-Net. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(6), 651\u2212659 (2021)","journal-title":"Comput Methods Biomech Biomed Eng: Imaging Visualization"},{"key":"172_CR36","first-page":"E106","volume":"296","author":"M Mossa-Basha","year":"2020","unstructured":"Mossa-Basha, M., et al.: Radiology department preparedness for COVID-19: radiology scientific expert review panel. Radiol Soc North Am 296, E106\u2013E112 (2020)","journal-title":"Radiol Soc North Am"},{"key":"172_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.108996","volume":"127","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., et al.: Radiology department strategies to protect radiologic technologists against COVID19: experience from Wuhan. Eur. J. Radiol. 127, 108996 (2020)","journal-title":"Eur. J. Radiol."},{"key":"172_CR38","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.clinimag.2020.06.031","volume":"68","author":"M Zanardo","year":"2020","unstructured":"Zanardo, M., Schiaffino, S., Sardanelli, F.: Bringing radiology to patient\u2019s home using mobile equipment: a weapon to fight COVID-19 pandemic. Clin. Imaging 68, 99\u2013101 (2020)","journal-title":"Clin. Imaging"},{"key":"172_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109272","volume":"132","author":"A Cozzi","year":"2020","unstructured":"Cozzi, A., et al.: Chest x-ray in the COVID-19 pandemic: radiologists\u2019 real-world reader performance. Eur. J. Radiol. 132, 109272 (2020)","journal-title":"Eur. J. Radiol."}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-022-00172-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00354-022-00172-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-022-00172-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T18:11:33Z","timestamp":1670350293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00354-022-00172-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["172"],"URL":"https:\/\/doi.org\/10.1007\/s00354-022-00172-4","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,6]]},"assertion":[{"value":"12 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2022","order":3,"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 not disclosed any 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"}]}}