{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T07:50:13Z","timestamp":1781423413116,"version":"3.54.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"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":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s42979-021-00782-7","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T14:02:51Z","timestamp":1627048971000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":162,"title":["DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2108-6918","authenticated-orcid":false,"given":"Najmul","family":"Hasan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5418-8799","authenticated-orcid":false,"given":"Yukun","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashadullah","family":"Shawon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanmei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"issue":"8","key":"782_CR1","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1111\/anae.15116","volume":"75","author":"E Kursumovic","year":"2020","unstructured":"Kursumovic E, Lennane S, Cook TM. Deaths in healthcare workers due to COVID-19: the need for robust data and analysis. Anaesthesia. 2020;75(8):989\u201392.","journal-title":"Anaesthesia"},{"issue":"6","key":"782_CR2","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10140-020-01833-x","volume":"27","author":"M Mirza-Aghazadeh-Attari","year":"2020","unstructured":"Mirza-Aghazadeh-Attari M, et al. Predictors of coronavirus disease 19 (COVID-19) pneumonitis outcome based on computed tomography (CT) imaging obtained prior to hospitalization: a retrospective study. Emerg Radiol. 2020;27(6):653\u201361.","journal-title":"Emerg Radiol"},{"issue":"6","key":"782_CR3","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s10140-020-01802-4","volume":"27","author":"S Behzad","year":"2020","unstructured":"Behzad S, et al. Coronavirus disease 2019 (COVID-19) pneumonia incidentally detected on coronary CT angiogram: a do-not-miss diagnosis. Emerg Radiol. 2020;27(6):721\u20136.","journal-title":"Emerg Radiol"},{"key":"782_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100228","volume":"11","author":"N Hasan","year":"2020","unstructured":"Hasan N. A methodological approach for predicting COVID-19 epidemic using EEMD-ANN hybrid model. Internet Things. 2020;11: 100228.","journal-title":"Internet Things"},{"key":"782_CR5","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, et al. COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J Adv Res. 2020;24:91\u20138.","journal-title":"J Adv Res"},{"key":"782_CR6","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.cca.2020.03.009","volume":"505","author":"R Liu","year":"2020","unstructured":"Liu R, et al. Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta. 2020;505:172\u20135.","journal-title":"Clin Chim Acta"},{"key":"782_CR7","doi-asserted-by":"publisher","first-page":"28716","DOI":"10.1109\/ACCESS.2021.3058854","volume":"9","author":"D Konar","year":"2021","unstructured":"Konar D, et al. Auto-diagnosis of COVID-19 using lung CT Images with semi-supervised shallow learning network. IEEE Access. 2021;9:28716\u201328.","journal-title":"IEEE Access"},{"issue":"7","key":"782_CR8","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","volume":"39","author":"D Singh","year":"2020","unstructured":"Singh D, et al. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis. 2020;39(7):1379\u201389.","journal-title":"Eur J Clin Microbiol Infect Dis"},{"key":"782_CR9","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai T, et al. Correlation of chest CT and RT-PCR testing in Coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296: 200642.","journal-title":"Radiology"},{"issue":"1","key":"782_CR10","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10489-020-01826-w","volume":"51","author":"S Ahuja","year":"2021","unstructured":"Ahuja S, et al. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell. 2021;51(1):571\u201385.","journal-title":"Appl Intell"},{"issue":"2","key":"782_CR11","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.bsheal.2020.05.002","volume":"2","author":"AK Giri","year":"2020","unstructured":"Giri AK, Rana DR. Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study. Biosaf Health. 2020;2(2):53\u20136.","journal-title":"Biosaf Health"},{"key":"782_CR12","unstructured":"Rajinikanth V, Dey N, Raj ANJ, Hassanien AE, Santosh KC, Raja  N (2020) Harmony-search and otsu based system for coronavirus disease (COVID-19) detection using lung CT scan images. arXiv:2004.03431"},{"key":"782_CR13","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200432","volume":"296","author":"Y Fang","year":"2020","unstructured":"Fang Y, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020;296: 200432.","journal-title":"Radiology"},{"key":"782_CR14","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Cham: Springer; 2012."},{"key":"782_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101093","volume":"58","author":"J Qin","year":"2020","unstructured":"Qin J, et al. A biological image classification method based on improved CNN. Ecol Inform. 2020;58: 101093.","journal-title":"Ecol Inform"},{"key":"782_CR16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.procs.2018.10.327","volume":"140","author":"RD Gottapu","year":"2018","unstructured":"Gottapu RD, Dagli CH. DenseNet for anatomical brain segmentation. Proced Comput Sci. 2018;140:179\u201385.","journal-title":"Proced Comput Sci"},{"issue":"4","key":"782_CR17","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1016\/j.bbe.2020.08.005","volume":"40","author":"B Abraham","year":"2020","unstructured":"Abraham B, Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybern Biomed Eng. 2020;40(4):1436\u201345.","journal-title":"Biocybern Biomed Eng"},{"key":"782_CR18","doi-asserted-by":"publisher","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 Progr Biomed. 2020;196: 105581.","journal-title":"Comput Methods Progr Biomed"},{"key":"782_CR19","first-page":"497","volume":"395","author":"C Zheng","year":"2020","unstructured":"Zheng C, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv. 2020;395:497.","journal-title":"medRxiv"},{"key":"782_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110190","volume":"140","author":"H Panwar","year":"2020","unstructured":"Panwar H, et al. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals. 2020;140: 110190.","journal-title":"Chaos Solitons Fractals"},{"key":"782_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110245","volume":"140","author":"C Ouchicha","year":"2020","unstructured":"Ouchicha C, Ammor O, Meknassi M. CVDNet: a novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos Solitons Fractals. 2020;140: 110245.","journal-title":"Chaos Solitons Fractals"},{"key":"782_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Medi. 2020;121: 103792.","journal-title":"Comput Biol Medi"},{"key":"782_CR23","doi-asserted-by":"crossref","unstructured":"Deng J, et al. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE. 2009.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"782_CR24","doi-asserted-by":"crossref","unstructured":"Huang G, et al. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"782_CR25","volume-title":"Conference on artificial intelligence in medicine in Europe","author":"S Roy","year":"2019","unstructured":"Roy S, Kiral-Kornek I, Harrer S. ChronoNet: a deep recurrent neural network for abnormal EEG identification. In: Conference on artificial intelligence in medicine in Europe. Cham: Springer; 2019."},{"issue":"21","key":"782_CR26","doi-asserted-by":"publisher","first-page":"30615","DOI":"10.1007\/s11042-018-6535-y","volume":"78","author":"W Guo","year":"2019","unstructured":"Guo W, Xu Z, Zhang H. Interstitial lung disease classification using improved DenseNet. Multimed Tools Appl. 2019;78(21):30615\u201326.","journal-title":"Multimed Tools Appl"},{"key":"782_CR27","volume-title":"International conference on computational intelligence in communications and business analytics","author":"T Sarkar","year":"2021","unstructured":"Sarkar T, Hazra A, Das N. Classification of colorectal cancer histology images using image reconstruction and modified DenseNet. In: International conference on computational intelligence in communications and business analytics. Cham: Springer; 2021."},{"key":"782_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101935","volume":"108","author":"L Gao","year":"2020","unstructured":"Gao L, et al. Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif Intell Med. 2020;108: 101935.","journal-title":"Artif Intell Med"},{"key":"782_CR29","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ins.2019.01.012","volume":"482","author":"B Lodhi","year":"2019","unstructured":"Lodhi B, Kang J. Multipath-DenseNet: a supervised ensemble architecture of densely connected convolutional networks. Inf Sci. 2019;482:63\u201372.","journal-title":"Inf Sci"},{"key":"782_CR30","volume-title":"Image analysis and processing\u2013ICIAP 2019","author":"P Carcagn\u00ec","year":"2019","unstructured":"Carcagn\u00ec P, et al. Classification of skin lesions by combining multilevel learnings in a DenseNet architecture. In: Image analysis and processing\u2013ICIAP 2019. Cham: Springer International Publishing; 2019."},{"key":"782_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105532","volume":"194","author":"RM Pereira","year":"2020","unstructured":"Pereira RM, et al. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Progr Biomed. 2020;194: 105532.","journal-title":"Comput Methods Progr Biomed"},{"key":"782_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109944","volume":"138","author":"H Panwar","year":"2020","unstructured":"Panwar H, et al. 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"},{"key":"782_CR33","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","volume":"39","author":"X Wang","year":"2020","unstructured":"Wang X, et al. A weakly-supervised Framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging. 2020;39:2615\u201325.","journal-title":"IEEE Trans Med Imaging"},{"key":"782_CR34","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.24.20078584","author":"E Soares","year":"2020","unstructured":"Soares E, et al. SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv. 2020. https:\/\/doi.org\/10.1101\/2020.04.24.20078584.","journal-title":"medRxiv"},{"key":"782_CR35","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. 2020;140: 109761.","journal-title":"Med Hypotheses"},{"key":"782_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2020.05.003","author":"Y Pathak","year":"2020","unstructured":"Pathak Y, et al. Deep transfer learning based classification model for COVID-19 disease. IRBM. 2020. https:\/\/doi.org\/10.1016\/j.irbm.2020.05.003.","journal-title":"IRBM"},{"key":"782_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106270","volume":"205","author":"WM Shaban","year":"2020","unstructured":"Shaban WM, et al. A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl-Based Systems. 2020;205: 106270.","journal-title":"Knowl-Based Systems"},{"issue":"10","key":"782_CR38","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2019","unstructured":"Xu X, et al. A deep learning system to screen novel Coronavirus Disease 2019 pneumonia. Engineering. 2019;6(10):1122\u20131129. https:\/\/doi.org\/10.1016\/j.eng.2020.04.010.","journal-title":"Engineering"},{"key":"782_CR39","doi-asserted-by":"publisher","unstructured":"Wang S, Kang, B, Ma J et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Eur Radiol 2021;31:6096\u20136104. https:\/\/doi.org\/10.1007\/s00330-021-07715-1.","DOI":"10.1007\/s00330-021-07715-1"},{"key":"782_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107645","volume":"110","author":"S Albahli","year":"2021","unstructured":"Albahli S, Ayub N, Shiraz M. Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet. Appl Soft Comput. 2021;110: 107645.","journal-title":"Appl Soft Comput"},{"issue":"1","key":"782_CR41","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60.","journal-title":"J Big Data"},{"key":"782_CR42","unstructured":"Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw Vis Recognit 2017;11:1\u20138."},{"issue":"4","key":"782_CR43","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1007\/s11517-020-02299-2","volume":"59","author":"M Singh","year":"2021","unstructured":"Singh M, et al. Transfer learning\u2013based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data. Med Biol Eng Comput. 2021;59(4):825\u201339.","journal-title":"Med Biol Eng Comput"},{"key":"782_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100427","volume":"20","author":"P Silva","year":"2020","unstructured":"Silva P, et al. COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inform Med Unlocked. 2020;20: 100427.","journal-title":"Inform Med Unlocked"},{"key":"782_CR45","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.comcom.2021.06.011","volume":"176","author":"S Fouladi","year":"2021","unstructured":"Fouladi S, et al. Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio. Comput Commun. 2021;176:234\u201348.","journal-title":"Comput Commun"},{"key":"782_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2020.07.001","author":"N Narayan Das","year":"2020","unstructured":"Narayan Das N, et al. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. IRBM. 2020. https:\/\/doi.org\/10.1016\/j.irbm.2020.07.001.","journal-title":"IRBM"},{"key":"782_CR47","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3065361","author":"Y Song","year":"2020","unstructured":"Song Y, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv. 2020. https:\/\/doi.org\/10.1109\/TCBB.2021.3065361.","journal-title":"medRxiv"},{"issue":"4","key":"782_CR48","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"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00782-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00782-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00782-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T14:11:22Z","timestamp":1630332682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00782-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["782"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00782-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-96782\/v1","asserted-by":"object"}]},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]},"assertion":[{"value":"13 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 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":"There is no potential conflict of interest declared by all authors.","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"}],"article-number":"389"}}