{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T03:57:03Z","timestamp":1793937423920,"version":"4.8.0"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1016\/j.asoc.2021.107160","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T01:46:11Z","timestamp":1612748771000},"page":"107160","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":102,"special_numbering":"C","title":["DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images"],"prefix":"10.1016","volume":"103","author":[{"given":"Fatih","family":"Demir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2021.107160_b1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","article-title":"A new coronavirus associated with human respiratory disease in China","volume":"579","author":"Wu","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.asoc.2021.107160_b2","series-title":"Coronavirus: covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate","author":"Mahase","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b3","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study","volume":"395","author":"Chen","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.asoc.2021.107160_b4","first-page":"1843","article-title":"Detection of SARS-CoV-2 in different types of clinical specimens","volume":"323","author":"Wang","year":"2020","journal-title":"JAMA - J. Am. Med. Assoc."},{"key":"10.1016\/j.asoc.2021.107160_b5","doi-asserted-by":"crossref","DOI":"10.2807\/1560-7917.ES.2020.25.3.2000045","article-title":"Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR","volume":"25","author":"Corman","year":"2020","journal-title":"Eurosurveillance"},{"key":"10.1016\/j.asoc.2021.107160_b6","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1148\/radiol.2020200463","article-title":"Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection","volume":"295","author":"Bernheim","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.asoc.2021.107160_b7","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology."},{"key":"10.1016\/j.asoc.2021.107160_b8","doi-asserted-by":"crossref","first-page":"E41","DOI":"10.1148\/radiol.2020200343","article-title":"Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: Relationship to negative RT-PCR testing","volume":"296","author":"Xie","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.asoc.2021.107160_b9","series-title":"AI-Assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks","author":"Jin","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b10","series-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","author":"Narin","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b11","article-title":"COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2021.107160_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2020.104054","article-title":"An automated Residual Exemplar Local Binary Pattern and iterative Relief based corona detection method using lung X-ray image","volume":"203","author":"Tuncer","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.asoc.2021.107160_b13","series-title":"IEEE Reg. 10 Annu. Int. Conf. Proceedings\/TENCON","first-page":"182","article-title":"Lung disease classification using reticular pattern scoring and five class features with greedy stepwise based on GLCM","author":"Ming","year":"2017"},{"key":"10.1016\/j.asoc.2021.107160_b14","article-title":"A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images","volume":"168","author":"Varela-Santos","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2021.107160_b15","series-title":"Adv. Comput. Vis. Pattern Recognit.","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/978-3-030-13969-8_18","article-title":"ChestX-ray: Hospital-scale chest X-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases","author":"Wang","year":"2019"},{"key":"10.1016\/j.asoc.2021.107160_b16","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","article-title":"Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks","volume":"43","author":"Apostolopoulos","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.asoc.2021.107160_b17","article-title":"Deep transfer learning based classification model for COVID-19 disease","author":"Pathak","year":"2020","journal-title":"Irbm."},{"key":"10.1016\/j.asoc.2021.107160_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103805","article-title":"COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches","volume":"121","author":"To\u011fa\u00e7ar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2021.107160_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114054","article-title":"Deep learning approaches for COVID-19 detection based on chest X-ray images","volume":"164","author":"Ismael","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2021.107160_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103795","article-title":"Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks","volume":"121","author":"Ardakani","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2021.107160_b21","article-title":"COVIdiagnosis-net: Deep Bayes-squeezenet based diagnostic of the coronavirus disease 2019 (COVID-19) from X-ray images","volume":"140","author":"Ucar","year":"2019","journal-title":"Med. Hypotheses."},{"key":"10.1016\/j.asoc.2021.107160_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2021.107160_b23","first-page":"1","article-title":"The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection","volume":"8","author":"Ismael","year":"2020","journal-title":"Heal. Inf. Sci. Syst."},{"key":"10.1016\/j.asoc.2021.107160_b24","unstructured":"COVID-19 X-ray images, (n.d.). https:\/\/www.kaggle.com\/bachrr\/covid-chest-xray."},{"key":"10.1016\/j.asoc.2021.107160_b25","unstructured":"Chest X-ray Images (Pneumonia), (n.d.). https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia."},{"key":"10.1016\/j.asoc.2021.107160_b26","doi-asserted-by":"crossref","first-page":"351","DOI":"10.14358\/PERS.70.3.351","article-title":"Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery","volume":"70","author":"Wang","year":"2004","journal-title":"Photogramm. Eng. Remote Sensing."},{"key":"10.1016\/j.asoc.2021.107160_b27","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1109\/JSTARS.2018.2830410","article-title":"Individual tree crown detection and delineation from very-high-resolution UAV images based on bias field and marker-controlled watershed segmentation algorithms","volume":"11","author":"Huang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.asoc.2021.107160_b28","series-title":"Adv. Neural Inf. Process. Syst.","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.asoc.2021.107160_b29","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.asoc.2021.107160_b30","doi-asserted-by":"crossref","first-page":"66529","DOI":"10.1109\/ACCESS.2020.2984903","article-title":"A new deep CNN model for environmental sound classification","volume":"8","author":"Demir","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2021.107160_b31","first-page":"252","article-title":"Classification of heart sounds based on convolutional neural network","volume":"819","author":"Li","year":"2018","journal-title":"Commun. Comput. Inf. Sci."},{"key":"10.1016\/j.asoc.2021.107160_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2020.107520","article-title":"A new pyramidal concatenated CNN approach for environmental sound classification","volume":"170","author":"Demir","year":"2020","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.asoc.2021.107160_b33","unstructured":"S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: 32nd Int. Conf. Mach. Learn., Vol. 1, ICML 2015, 2015, pp. 448\u2013456."},{"key":"10.1016\/j.asoc.2021.107160_b34","doi-asserted-by":"crossref","first-page":"105376","DOI":"10.1109\/ACCESS.2020.3000111","article-title":"Classification of lung sounds with CNN model using parallel pooling structure","volume":"8","author":"Demir","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2021.107160_b35","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.asoc.2021.107160_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105765","article-title":"Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images","volume":"85","author":"Budak","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"10.1016\/j.asoc.2021.107160_b37","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2021.107160_b38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2019.2909119","article-title":"Robust approach based on convolutional neural networks for identification of focal EEG signals","volume":"3","author":"Bajaj","year":"2019","journal-title":"IEEE Sensors Lett."},{"key":"10.1016\/j.asoc.2021.107160_b39","first-page":"643","article-title":"Detection of coronavirus disease (COVID-19) based on deep features and support vector machine","volume":"5","author":"Sethy","year":"2020","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"10.1016\/j.asoc.2021.107160_b40","series-title":"COVIDX-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images","author":"Hemdan","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b41","series-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","author":"Song","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b42","series-title":"A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)","author":"Wang","year":"2020"},{"key":"10.1016\/j.asoc.2021.107160_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos, Solitons and Fractals."},{"key":"10.1016\/j.asoc.2021.107160_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106580","article-title":"A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization","volume":"97","author":"Nour","year":"2020","journal-title":"Appl. Soft Comput. J."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494621000831?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494621000831?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T19:51:32Z","timestamp":1761594692000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494621000831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5]]},"references-count":44,"alternative-id":["S1568494621000831"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2021.107160","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2021,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2021.107160","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107160"}}