{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:08:07Z","timestamp":1791482887003,"version":"4.3.4"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"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":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1016\/j.bspc.2022.104445","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T04:30:15Z","timestamp":1669782615000},"page":"104445","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":95,"special_numbering":"C","title":["PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates"],"prefix":"10.1016","volume":"81","author":[{"given":"Yogesh H.","family":"Bhosale","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Sridhar","family":"Patnaik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2022.104445_b0005","unstructured":"Coronavirus Disease 2019. [Online]. Available: https:\/\/en.wikipedia.org\/wiki\/Coronavirus_disease_2019 ."},{"key":"10.1016\/j.bspc.2022.104445_b0010","unstructured":"Summers, Ronald (Lung Disease X-Ray Dataset). CXR8 - National Institutes of Health - Clinical Center. Accessed On 12 September 2021. Online Available https:\/\/nihcc.app.box.com\/v\/ChestXray-NIHCC\/folder\/37178474737."},{"key":"10.1016\/j.bspc.2022.104445_b0015","unstructured":"Paul Mooney (Chest X-Ray Pneumonia Dataset). Accessed On 12 September 2021. [Online]. Available: https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia."},{"key":"10.1016\/j.bspc.2022.104445_b0020","unstructured":"SIIM-FISABIO-RSNA COVID-19 Detection Challenge. Accessed On 12 September 2021. [Online]. Available: https:\/\/www.kaggle.com\/c\/siim-covid19-detection."},{"key":"10.1016\/j.bspc.2022.104445_b0025","unstructured":"Who coronavirus disease (covid-19) dashboard, (https:\/\/covid19.who.int\/). Accessed On 09 January 2022."},{"key":"10.1016\/j.bspc.2022.104445_b0030","series-title":"Proc. Int. Conf","first-page":"1","article-title":"Understanding of a convolutional neural network","author":"Albawi","year":"2017"},{"key":"10.1016\/j.bspc.2022.104445_b0035","doi-asserted-by":"crossref","first-page":"113833","DOI":"10.1016\/j.eswa.2020.113833","article-title":"Detecting the shuttlecock for a badminton robot: A YOLO based approach","volume":"164","author":"Cao","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.bspc.2022.104445_b0040","doi-asserted-by":"crossref","first-page":"107499","DOI":"10.1016\/j.patcog.2020.107499","article-title":"Cost-sensitive deep forest for price prediction","volume":"107","author":"Ma","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.bspc.2022.104445_b0045","unstructured":"S. Chen, E. Dobriban, and J. H. Lee, \u201cInvariance reduces variance: Understanding data augmentation in deep learning and beyond,\u201d 2019, arXiv:1907.10905."},{"key":"10.1016\/j.bspc.2022.104445_b0050","doi-asserted-by":"crossref","unstructured":"D. Jakubovitz, R. Giryes, and M. R. Rodrigues, \u201cGeneralization error in deep learning,\u201d in Compressed Sensing and Its Applications, Springer, 2019, pp. 153\u2013193.","DOI":"10.1007\/978-3-319-73074-5_5"},{"key":"10.1016\/j.bspc.2022.104445_b0055","series-title":"Ensemble Machine Learning","first-page":"1","article-title":"\u201cEnsemble learning,\u201d in Ensemble Machine Learning","author":"Polikar","year":"2012"},{"key":"10.1016\/j.bspc.2022.104445_b0060","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI Help in Screening Viral and COVID-19 Pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.bspc.2022.104445_b0065","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1021\/ci0500379","article-title":"\u201cBoosting: An ensemble learning tool for compound classification and QSAR modeling","volume":"45","author":"Svetnik","year":"2005","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.bspc.2022.104445_b0070","doi-asserted-by":"crossref","unstructured":"F. Divina, A. Gilson, F. Gom\u00e9z-Vela, M. Garc\u00eda Torres, and J. F. Torres, \u201cStacking ensemble learning for short-term electricity consumption forecasting,\u201d Energies, vol. 11, no. 4, 2018, Art. No. 949.","DOI":"10.3390\/en11040949"},{"key":"10.1016\/j.bspc.2022.104445_b0075","unstructured":"H. Wong et al., \u201cFrequency and distribution of chest radiographic findings in COVID-19 positive patients\u201d, Radiology, 2020."},{"issue":"7","key":"10.1016\/j.bspc.2022.104445_b0080","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1002\/jmv.25786","article-title":"Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19","volume":"92","author":"Li","year":"2020","journal-title":"J. Med. Virol."},{"key":"10.1016\/j.bspc.2022.104445_b0085","unstructured":"Ensemble techniques in python. Accessed on: 15 December 2021. [Online]. Available: https:\/\/www.geeksforgeeks.org\/ensemble-methods-in-python\/."},{"key":"10.1016\/j.bspc.2022.104445_b0090","unstructured":"Stacked Ensemble technique. Accessed on: 15 December 2021. [Online]. Available: https:\/\/machinelearningmastery.com\/stacking-ensemble-machine-learning-with-python\/."},{"key":"10.1016\/j.bspc.2022.104445_b0095","unstructured":"NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories. Accessed On 2 December 2021. [Online]. Available: https:\/\/nihcc.app.box.com\/v\/ChestXray-NIHCC."},{"key":"10.1016\/j.bspc.2022.104445_b0100","unstructured":"G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger, \u201cSnapshot ensembles: Train 1, get M for free,\u201d 2017, arXiv:1704.00109."},{"key":"10.1016\/j.bspc.2022.104445_b0105","series-title":"In: Hands-on machine learning with scikit-learn, keras, and TensorFlow","author":"Geron","year":"2019"},{"issue":"8","key":"10.1016\/j.bspc.2022.104445_b0110","doi-asserted-by":"crossref","first-page":"PMC6690680","DOI":"10.1371\/journal.pone.0220294","article-title":"A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG","volume":"14","author":"Ledezma","year":"2019","journal-title":"PLoS ONE"},{"key":"10.1016\/j.bspc.2022.104445_b0115","doi-asserted-by":"crossref","first-page":"100158","DOI":"10.1016\/j.lanepe.2021.100158","article-title":"Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people","volume":"8","author":"Semenzato","year":"2021","journal-title":"The Lancet Regional Health - Europe"},{"key":"10.1016\/j.bspc.2022.104445_b0120","doi-asserted-by":"crossref","DOI":"10.3389\/fmed.2021.588013","article-title":"Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review with Cumulative Meta-Analysis","volume":"8","author":"Geng","year":"2021","journal-title":"Front. Med."},{"key":"10.1016\/j.bspc.2022.104445_b0125","doi-asserted-by":"crossref","unstructured":"H. S. Maghdid, A. T. Asaad, K. Z. Ghafoor, A. S. Sadiq, and M. K. Khan, \u201cDiagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms,\u201d 2020, arXiv:2004.00038.","DOI":"10.1117\/12.2588672"},{"issue":"1","key":"10.1016\/j.bspc.2022.104445_b0130","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.bspc.2022.104445_b0135","doi-asserted-by":"crossref","first-page":"106647","DOI":"10.1016\/j.knosys.2020.106647","article-title":"FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection","volume":"212","author":"Abdel-Basset","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.bspc.2022.104445_b0140","unstructured":"Hall L.O., Paul R., Goldgof D.B., Goldgof G.M.. Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004020602020."},{"issue":"10","key":"10.1016\/j.bspc.2022.104445_b0145","doi-asserted-by":"crossref","first-page":"6863","DOI":"10.1007\/s00521-018-3518-x","article-title":"Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans","volume":"31","author":"Singh","year":"2019","journal-title":"Neural Computing Applications"},{"issue":"3","key":"10.1016\/j.bspc.2022.104445_b0150","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.21037\/jtd.2018.02.76","article-title":"Deep learning and medical imaging","volume":"10","author":"Klang","year":"2018","journal-title":"J Thorac Dis"},{"key":"10.1016\/j.bspc.2022.104445_b0155","unstructured":"Shan F., Gao Y., Wang J., Shi W., Shi N., Han M., et al. Lung infection quantification of covid-19 in ct images with deep learning. arXiv preprint arXiv:2003046552020."},{"issue":"1","key":"10.1016\/j.bspc.2022.104445_b0160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","article-title":"Applications of deep learning to mri images: asurvey","volume":"1","author":"Liu","year":"2018","journal-title":"Big Data Min Anal"},{"key":"10.1016\/j.bspc.2022.104445_b0165","doi-asserted-by":"crossref","first-page":"115041","DOI":"10.1109\/ACCESS.2020.3003810","article-title":"Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays","volume":"8","author":"Rajaraman","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2022.104445_b0170","unstructured":"American Lung Association. Accessed On 12 September 2021. [Online]. Available: https:\/\/www.lung.org\/lung-health-diseases\/lung-disease-lookup\/covid-19\/chronic-lung-diseases-and-covid."},{"issue":"8","key":"10.1016\/j.bspc.2022.104445_b0175","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1016\/S2213-2600(21)00095-3","article-title":"Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study","volume":"9","author":"Aveyard","year":"2021","journal-title":"The Lancet Respiratory Medicine"},{"key":"10.1016\/j.bspc.2022.104445_b0180","unstructured":"Zabirul Islam, Milon Islam,, Amanullah Asraf. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images."},{"key":"10.1016\/j.bspc.2022.104445_b0185","doi-asserted-by":"crossref","unstructured":"Rahimzadeh M, Attar A. A new modified deep convolutional neural network for detecting COVID-19 from X-ray images. http:\/\/arxiv.org\/abs\/2004.08052; 2020.","DOI":"10.1016\/j.imu.2020.100360"},{"key":"10.1016\/j.bspc.2022.104445_b0190","doi-asserted-by":"crossref","unstructured":"Alqudah AM, Qazan S, Alquran HH, Alquran H, Qasmieh IA, Alqudah A. Covid-2019 detection using X-ray images and artificial intelligence hybrid systems. https:\/\/doi.org\/10.13140\/RG.2.2.16077.59362\/1 ; 2020.","DOI":"10.5455\/jjee.204-1585312246"},{"key":"10.1016\/j.bspc.2022.104445_b0195","unstructured":"Hemdan EE-D, Shouman MA, Karar ME. COVIDX-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. http:\/\/arxiv.org\/abs\/2003.11055 ; 2020."},{"issue":"10","key":"10.1016\/j.bspc.2022.104445_b0200","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1080\/07391102.2020.1767212","article-title":"Using X-ray images and deep learning for automated detection of coronavirus disease","volume":"39","author":"El Asnaoui","year":"2021","journal-title":"J Biomol Struct Dyn"},{"key":"10.1016\/j.bspc.2022.104445_b0205","doi-asserted-by":"crossref","unstructured":"Ningwei Wang, Hongzhe Liu*, Cheng Xu. Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration. IEEE Conference (ICEIEC); https:\/\/doi.org\/10.1109\/ICEIEC49280.2020.9152329 ; 2020.","DOI":"10.1109\/ICEIEC49280.2020.9152329"},{"issue":"3","key":"10.1016\/j.bspc.2022.104445_b0210","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal Applic"},{"key":"10.1016\/j.bspc.2022.104445_b0215","doi-asserted-by":"crossref","unstructured":"F. Ucar, D. Korkmaz, COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diag nosis of the coronavirus disease 2019 (COVID-19) from X-Ray images, Med.Hypotheses (2020) 109761.","DOI":"10.1016\/j.mehy.2020.109761"},{"key":"10.1016\/j.bspc.2022.104445_b0220","doi-asserted-by":"crossref","first-page":"103792","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.bspc.2022.104445_b0225","doi-asserted-by":"crossref","unstructured":"Mohit Mishra, Varun Parashar, Rushikesh Shimpi. Development and evaluation of an AI System for early detection of Covid-19 pneumonia using X-ray. https:\/\/doi.org\/10.1109\/BigMM50055.2020.00051 .","DOI":"10.1109\/BigMM50055.2020.00051"},{"key":"10.1016\/j.bspc.2022.104445_b0230","doi-asserted-by":"crossref","first-page":"651","DOI":"10.3390\/sym12040651","article-title":"Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning","volume":"12","author":"Loey","year":"2020","journal-title":"Symmetry"},{"issue":"9","key":"10.1016\/j.bspc.2022.104445_b0235","article-title":"EDL-COVID: Ensemble Deep Learning For COVID-19 Cases Detection From Chest X-ray Iimages. IEEE Transactions On Industrial","volume":"17","author":"Tang","year":"2021","journal-title":"Info"},{"key":"10.1016\/j.bspc.2022.104445_b0240","unstructured":"T. Zhou, H.L. Lu, Z. Yang et al. The ensemble deep learning model for novel COVID-19 on CT images. https:\/\/doi.org\/10.1016\/j.asoc.2020.1068851568-4946\/ ."},{"key":"10.1016\/j.bspc.2022.104445_b0245","unstructured":"Covid-19: Omicron less severe than Delta variant, UK studies find. Accessed On 12 September 2021. [Online]. Available: https:\/\/www.business-standard.com\/article\/current-affairs\/covid-19-omicron-less-severe-than-delta-variant-uk-studies-find-121122300641_1.html."},{"issue":"5","key":"10.1016\/j.bspc.2022.104445_b0250","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/14737159.2020.1757437","article-title":"Real-time RT-PCR in COVID-19 detection: Issues affecting the results","volume":"10","author":"Tahamtan","year":"2020","journal-title":"Expert Rev. Mol. Diagn."},{"key":"10.1016\/j.bspc.2022.104445_b0255","unstructured":"Bhosale YH, Patnaik KS. Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review. Neural Process Lett. 2022 Sep 16:1-53. DOI: 10.1007\/s11063-022-11023-0. Epub ahead of print. PMID: 36158520; PMCID: PMC9483290."},{"key":"10.1016\/j.bspc.2022.104445_b0260","doi-asserted-by":"crossref","unstructured":"Bhosale Y.H., Patnaik K.S, IoT Deployable Lightweight Deep Learning Application For COVID-19 Detection With Lung Diseases Using RaspberryPi; IEEE-ICIBT-2022, pp. 1-6.","DOI":"10.1109\/ICIBT52874.2022.9807725"},{"key":"10.1016\/j.bspc.2022.104445_b0265","doi-asserted-by":"crossref","unstructured":"Y. H. Bhosale, et al., Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices; IEEE-ICACCS-2022, pp. 1398-1402.","DOI":"10.1109\/ICACCS54159.2022.9785113"},{"issue":"8","key":"10.1016\/j.bspc.2022.104445_b0270","doi-asserted-by":"crossref","first-page":"8551","DOI":"10.1007\/s10489-021-02945-8","article-title":"Decision and feature level fusion of deep features extracted from public COVID-19 data-sets","volume":"52","author":"Ilhan","year":"2022","journal-title":"Applied Intelligence"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809422008990?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809422008990?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T19:35:15Z","timestamp":1761680115000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809422008990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":54,"alternative-id":["S1746809422008990"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2022.104445","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2022.104445","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"104445"}}