{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:26:49Z","timestamp":1783528009670,"version":"3.55.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model\u2019s performance. From the external test dataset, we calculated the model\u2019s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s13755-021-00166-4","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T05:45:01Z","timestamp":1634103901000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2768-5340","authenticated-orcid":false,"given":"Vasilis","family":"Nikolaou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastiano","family":"Massaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masoud","family":"Fakhimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lampros","family":"Stergioulas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Garn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,12]]},"reference":[{"key":"166_CR1","unstructured":"COVID-19 pandemic. https:\/\/en.wikipedia.org\/wiki\/COVID-19_pandemic. Accessed on 09 May, 2021"},{"key":"166_CR2","unstructured":"COVID-19 pandemic. https:\/\/en.wikipedia.org\/wiki\/COVID-19_pandemic#Transmission. Accessed on 09 May, 2021"},{"key":"166_CR3","unstructured":"Reverse transcription polymerase chain reaction. https:\/\/en.wikipedia.org\/wiki\/Reverse_transcription_polymerase_chain_reaction. Accessed on 09 May, 2021"},{"key":"166_CR4","unstructured":"COVID-19 pandemic. https:\/\/en.wikipedia.org\/wiki\/COVID-19_pandemic#Diagnosis. Accessed on 09 May, 2021"},{"issue":"1","key":"166_CR5","first-page":"e200034","volume":"2","author":"M-Y Ng","year":"2020","unstructured":"Ng M-Y, Lee EYP, Yang J, et al. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology. 2020;2(1):e200034.","journal-title":"Radiology"},{"issue":"5","key":"166_CR6","doi-asserted-by":"publisher","first-page":"e20200226","DOI":"10.36416\/1806-3756\/e20200226","volume":"46","author":"E Baratella","year":"2020","unstructured":"Baratella E, Crivelli P, Marrocchio C, et al. Severity of lung involvement on chest X-rays in SARS-coronavirus-2 infected patients as a possible tool to predict clinical progression: an observational retrospective analysis of the relationship between radiological, clinical, and laboratory data. J Brasil Pneumol. 2020;46(5):e20200226.","journal-title":"J Brasil Pneumol"},{"issue":"1","key":"166_CR7","first-page":"172","volume":"296","author":"GD Rubin","year":"2020","unstructured":"Rubin GD, Ryerson CJ, Haramati LB, et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society. Chest. 2020;296(1):172\u201380.","journal-title":"Chest"},{"key":"166_CR8","unstructured":"ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection (https:\/\/www.acr.org\/Advocacy-and-Economics\/ACR-Position-Statements\/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection). Accessed on 09\/05\/2021."},{"issue":"2","key":"166_CR9","doi-asserted-by":"publisher","first-page":"E32","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. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296(2):E32-40.","journal-title":"Radiology"},{"key":"166_CR10","first-page":"2021","volume":"15","author":"M Ghaderzadeh","year":"2021","unstructured":"Ghaderzadeh M, Asadi F. Deep learning in the detection and diagnosis of COVID-19 using radiology modalities: a systematic review. J Healthcare Eng. 2021;15:2021.","journal-title":"J Healthcare Eng"},{"key":"166_CR11","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248\u2013255), (2009, June).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"166_CR12","doi-asserted-by":"crossref","unstructured":"Chaudhary Y, Mehta M, Sharma R, Gupta D, Khanna A, Rodrigues JJ. Efficient-CovidNet: deep learning based COVID-19 detection from chest x-ray images. In 2020 IEEE international conference on e-health networking, application & services (HEALTHCOM) 2021 Mar 1 (pp. 1\u20136). IEEE.","DOI":"10.1109\/HEALTHCOM49281.2021.9398980"},{"key":"166_CR13","first-page":"1","volume":"20","author":"E Luz","year":"2021","unstructured":"Luz E, Silva P, Silva R, Silva L, Guimar\u00e3es J, Miozzo G, Moreira G, Menotti D. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Res Biomed Eng. 2021;20:1\u20134.","journal-title":"Res Biomed Eng"},{"issue":"1","key":"166_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00135-3","volume":"9","author":"TD Pham","year":"2021","unstructured":"Pham TD. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Health Inf Sci Syst. 2021;9(1):1\u20131.","journal-title":"Health Inf Sci Syst"},{"key":"166_CR15","first-page":"4","volume":"6","author":"FA Saiz","year":"2020","unstructured":"Saiz FA, Barandiaran I. COVID-19 detection in chest X-ray images using a deep learning approach. Int J Interact Multimed Artif Intell. 2020;6:4.","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"166_CR16","doi-asserted-by":"publisher","first-page":"100360","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A. 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. Inf Med Unlocked. 2020;19:100360.","journal-title":"Inf Med Unlocked"},{"key":"166_CR17","doi-asserted-by":"publisher","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","volume":"138","author":"H Panwar","year":"2020","unstructured":"Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solitons Fractals. 2020;138:109944.","journal-title":"Chaos Solitons Fractals"},{"issue":"2","key":"166_CR18","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.diii.2020.11.008","volume":"102","author":"J Li","year":"2020","unstructured":"Li J, Xi L, Wang X, et al. Radiology indispensable for tracking COVID-19. Diagn Interv Imaging. 2020;102(2):69\u201375.","journal-title":"Diagn Interv Imaging"},{"key":"166_CR19","doi-asserted-by":"publisher","first-page":"105608","DOI":"10.1016\/j.cmpb.2020.105608","volume":"196","author":"L Brunese","year":"2020","unstructured":"Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed. 2020;196:105608.","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"166_CR20","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/sym12040651","volume":"12","author":"M Loey","year":"2020","unstructured":"Loey M, Smarandache F, Khalifa NEM. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry. 2020;12(4):651.","journal-title":"Symmetry"},{"key":"166_CR21","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID- 19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792.","journal-title":"Comput Biol Med"},{"key":"166_CR22","first-page":"3615","volume":"38","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. 2020;38:3615\u201326.","journal-title":"J Biomol Struct Dyn"},{"issue":"5","key":"166_CR23","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1007\/s12559-020-09751-3","volume":"12","author":"N Dey","year":"2020","unstructured":"Dey N, Rajinikanth V, Fong SJ, Kaiser MS, Mahmud M. Social group optimization-assisted Kapur\u2019s entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images. Cogn Comput. 2020;12(5):1011\u201323.","journal-title":"Cogn Comput"},{"key":"166_CR24","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1007\/s00264-020-04609-7","volume":"44","author":"S Vaid","year":"2020","unstructured":"Vaid S, Kalantar R, Bhandari M. Deep learning COVID-19 detection bias: accuracy through artificial intelligence. Int Orthopaed. 2020;44:1539\u201342.","journal-title":"Int Orthopaed"},{"key":"166_CR25","doi-asserted-by":"publisher","first-page":"109761","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. 2020;140:109761.","journal-title":"Med Hypotheses"},{"key":"166_CR26","doi-asserted-by":"publisher","first-page":"103805","DOI":"10.1016\/j.compbiomed.2020.103805","volume":"121","author":"M Toga\u00e7ar","year":"2020","unstructured":"Toga\u00e7ar M, Ergen B, C\u00f6mert Z. COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med. 2020;121:103805.","journal-title":"Comput Biol Med"},{"key":"166_CR27","doi-asserted-by":"publisher","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan AI, Shah JL, Bhat MM. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed. 2020;196:105581.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"166_CR28","doi-asserted-by":"publisher","first-page":"662","DOI":"10.18517\/ijaseit.10.2.11446","volume":"10","author":"F Martinez","year":"2020","unstructured":"Martinez F, Mar\u00ed\u0131nez F, Jacinto E. Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19. Int J Adv Sci Eng Inf Technol. 2020;10(2):662.","journal-title":"Int J Adv Sci Eng Inf Technol"},{"key":"166_CR29","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. CovidGAN: data augmentation using auxiliary classifier GAN for improved COVID-19 detection. IEEE Access. 2020;8:91916\u201323.","journal-title":"IEEE Access"},{"key":"166_CR30","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1016\/j.cmpb.2020.105532","volume":"194","author":"RM Pereira","year":"2020","unstructured":"Pereira RM, Bertolini D, Teixeira LO, Silla CN, Costa YMG. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed. 2020;194:1055.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"166_CR31","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA. COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635\u201340.","journal-title":"Phys Eng Sci Med"},{"issue":"6","key":"166_CR32","doi-asserted-by":"publisher","first-page":"e0235187","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. New machine learning method for image-based diagnosis of COVID-19. PLoS ONE. 2020;15(6):e0235187.","journal-title":"PLoS ONE"},{"issue":"4","key":"166_CR33","first-page":"643","volume":"5","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Behera SK, Ratha PK, Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int J Math Eng Manag Sci. 2020;5(4):643\u201351.","journal-title":"Int J Math Eng Manag Sci"},{"issue":"4","key":"166_CR34","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1097\/RTI.0000000000000532","volume":"35","author":"PH Yi","year":"2020","unstructured":"Yi PH, Kim TK, Lin CT. Generalizability of deep learning tuberculosis classifier to COVID-19 chest radiographs: new tricks for an old algorithm? J Thoracic Imaging. 2020;35(4):102\u20134.","journal-title":"J Thoracic Imaging"},{"issue":"3","key":"166_CR35","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","volume":"43","author":"D Das","year":"2020","unstructured":"Das D, Santosh KC, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med. 2020;43(3):915\u201325.","journal-title":"Phys Eng Sci Med"},{"key":"166_CR36","unstructured":"COVID-19 Radiography Database. https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database. Accessed 3 May 2021."},{"key":"166_CR37","unstructured":"Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning 2019 May 24 (pp. 6105\u20136114). PMLR."},{"issue":"5","key":"166_CR38","doi-asserted-by":"publisher","first-page":"e286","DOI":"10.1016\/S2589-7500(21)00039-X","volume":"3","author":"Z Jiao","year":"2021","unstructured":"Jiao Z, Choi JW, Halsey K, Tran TM, Hsieh B, Wang D, Eweje F, Wang R, Chang K, Wu J, Collins SA. Prognostication of patients with COVID-19 using artificial intelligence based on chest X-rays and clinical data: a retrospective study. The Lancet Digital Health. 2021;3(5):e286\u201394.","journal-title":"The Lancet Digital Health"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-021-00166-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-021-00166-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-021-00166-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T14:26:36Z","timestamp":1638541596000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-021-00166-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,12]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["166"],"URL":"https:\/\/doi.org\/10.1007\/s13755-021-00166-4","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,12]]},"assertion":[{"value":"15 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2021","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 author declares no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"36"}}