{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T22:40:44Z","timestamp":1774651244421,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s00521-022-08099-z","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T13:04:20Z","timestamp":1670418260000},"page":"8259-8279","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images"],"prefix":"10.1007","volume":"35","author":[{"given":"J. Arun","family":"Prakash","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6873-6469","authenticated-orcid":false,"given":"Vinayakumar","family":"Ravi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Sowmya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. P.","family":"Soman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"8099_CR1","doi-asserted-by":"crossref","unstructured":"Neupane B et al. (2010) Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults.\"\u00a0American journal of respiratory and critical care medicine\u00a0181(1):47\u201353","DOI":"10.1164\/rccm.200901-0160OC"},{"key":"8099_CR2","unstructured":"Ramezani M, Aemmi SZ, Moghadam ZE (2015) Factors affecting the rate of pediatric pneumonia in developing countries: a review and literature study. Int J Pediatrics 3(6.2):1173\u20131181"},{"key":"8099_CR3","doi-asserted-by":"crossref","unstructured":"Lee GE et al. (2010) National hospitalization trends for pediatric pneumonia and associated complications. Pediatrics 126(2):204\u2013213","DOI":"10.1542\/peds.2009-3109"},{"issue":"4","key":"8099_CR4","first-page":"323","volume":"7","author":"P Dean","year":"2018","unstructured":"Dean P, Florin TA (2018) Factors associated with pneumonia severity in children: a systematic review. J Pediatric Infect Dis Soc 7(4):323\u2013334","journal-title":"J Pediatric Infect Dis Soc"},{"issue":"4","key":"8099_CR5","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.irbm.2020.05.005","volume":"42","author":"MM Rahman","year":"2021","unstructured":"Rahman MM et al (2021) Machine learning based computer aided diagnosis of breast cancer utilizing anthropometric and clinical features. Irbm 42(4):215\u2013226","journal-title":"Irbm"},{"key":"8099_CR6","doi-asserted-by":"crossref","unstructured":"Cherradi B et al. (2021) Computer-aided diagnosis system for early prediction of atherosclerosis using machine learning and K-fold cross-validation. In: 2021 International congress of advanced technology and engineering (ICOTEN). IEEE","DOI":"10.1109\/ICOTEN52080.2021.9493524"},{"key":"8099_CR7","doi-asserted-by":"crossref","unstructured":"Qin ZZ et al. (2021) Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digital Health\u00a03(9):e543-e554","DOI":"10.1016\/S2589-7500(21)00116-3"},{"key":"8099_CR8","doi-asserted-by":"crossref","unstructured":"Kundaram SS, Ketki CP (2021) Deep learning-based alzheimer disease detection.\u00a0In: Proceedings of the fourth international conference on microelectronics, computing and communication systems. Springer, Singapore","DOI":"10.1007\/978-981-15-5546-6_50"},{"key":"8099_CR9","doi-asserted-by":"crossref","unstructured":"Perdomo O et al. (2019) Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput Methods Prog Biomed 178: 181\u2013189","DOI":"10.1016\/j.cmpb.2019.06.016"},{"key":"8099_CR10","doi-asserted-by":"crossref","unstructured":"Kermany DS et al. (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell\u00a0172(5):1122\u20131131","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"8099_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.06.023","volume":"187","author":"G Liang","year":"2020","unstructured":"Liang G, Zheng L (2020) A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput Methods Prog Biomed 187:104964","journal-title":"Comput Methods Prog Biomed"},{"key":"8099_CR12","unstructured":"Habib N, Hasan MM, Rahman MM (2020) Fusion of deep convolutional neural network with PCA and logistic regression for diagnosis of pediatric pneumonia on chest X-Rays.\u00a0Network Biol\u00a076"},{"key":"8099_CR13","doi-asserted-by":"crossref","unstructured":"Kora Venu S (2020) An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images.\u00a0arXiv e-prints\u00a0(2020): arXiv-2011","DOI":"10.5220\/0010377403900401"},{"key":"8099_CR14","doi-asserted-by":"crossref","unstructured":"Chouhan V et al. (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images.\u00a0Appl Sci 10(2):559","DOI":"10.3390\/app10020559"},{"key":"8099_CR15","unstructured":"Rajpurkar P et al. (2017) Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.\u00a0arXiv preprint arXiv:1711.05225\u00a0"},{"key":"8099_CR16","doi-asserted-by":"crossref","unstructured":"Saraiva AA et al. (2019) Models of learning to classify x-ray Images for the detection of pneumonia using neural networks. Bioimaging","DOI":"10.5220\/0007346600760083"},{"key":"8099_CR17","doi-asserted-by":"crossref","unstructured":"Saraiva AA et al. (2019) Classification of images of childhood pneumonia using convolutional neural networks.Bioimaging","DOI":"10.5220\/0007404301120119"},{"issue":"1","key":"8099_CR18","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1002\/ima.22501","volume":"31","author":"A Akgundogdu","year":"2021","unstructured":"Akgundogdu A (2021) Detection of pneumonia in chest X-ray images by using 2D discrete wavelet feature extraction with random forest. Int J Imaging Syst Technol 31(1):82\u201393","journal-title":"Int J Imaging Syst Technol"},{"key":"8099_CR19","doi-asserted-by":"crossref","unstructured":"Siddiqi R (2019) Automated pneumonia diagnosis using a customized sequential convolutional neural network. In: Proceedings of the 2019 3rd international conference on deep learning technologies","DOI":"10.1145\/3342999.3343001"},{"issue":"6","key":"8099_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00361-2","volume":"1","author":"R Siddiqi","year":"2020","unstructured":"Siddiqi R (2020) Efficient pediatric pneumonia diagnosis using depthwise separable convolutions. SN Comput Sci 1(6):1\u201315","journal-title":"SN Comput Sci"},{"key":"8099_CR21","doi-asserted-by":"crossref","unstructured":"Rahman T et al. (2020) Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci 10(9):3233","DOI":"10.3390\/app10093233"},{"key":"8099_CR22","doi-asserted-by":"crossref","unstructured":"El Asnaoui K, Chawki Y, Idri A (2021) Automated methods for detection and classification pneumonia based on x-ray images using deep learning. Artificial intelligence and blockchain for future cybersecurity applications. Springer, Cham, pp 257\u2013284","DOI":"10.1007\/978-3-030-74575-2_14"},{"key":"8099_CR23","doi-asserted-by":"crossref","unstructured":"Rahman T et al. (2021) Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Medicine\u00a0132:104319","DOI":"10.1016\/j.compbiomed.2021.104319"},{"issue":"2","key":"8099_CR24","doi-asserted-by":"publisher","first-page":"2442","DOI":"10.35940\/ijitee.B7017.129219","volume":"9","author":"C Rubini","year":"2019","unstructured":"Rubini C, Pavithra N (2019) Contrast enhancement of MRI images using AHE and CLAHE techniques. Int J Innov Technol Explor Eng 9(2):2442\u20132445","journal-title":"Int J Innov Technol Explor Eng"},{"key":"8099_CR25","doi-asserted-by":"crossref","unstructured":"Habib N et al. (2020) Ensemble of CheXNet and VGG-19 feature extractor with random forest classifier for pediatric pneumonia detection. SN Comput Sci 1(6):1\u20139","DOI":"10.1007\/s42979-020-00373-y"},{"key":"8099_CR26","doi-asserted-by":"crossref","unstructured":"Luj\u00e1n-Garc\u00eda JE et al. (2020) A transfer learning method for pneumonia classification and visualization. Appl Sci 10(8):2908","DOI":"10.3390\/app10082908"},{"key":"8099_CR27","doi-asserted-by":"crossref","unstructured":"Nahid A et al. (2020) A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network. Sensors\u00a020(12):3482","DOI":"10.3390\/s20123482"},{"key":"8099_CR28","doi-asserted-by":"crossref","unstructured":"Islam KT et al. (2020) A deep transfer learning framework for pneumonia detection from chest X-ray images. VISIGRAPP (5: VISAPP)","DOI":"10.5220\/0008927002860293"},{"key":"8099_CR29","doi-asserted-by":"crossref","unstructured":"Mahajan S et al. (2019)Towards evaluating performance of domain specific transfer learning for pneumonia detection from X-Ray images. In: 2019 IEEE 5th international conference for convergence in technology (I2CT). IEEE","DOI":"10.1109\/I2CT45611.2019.9033555"},{"key":"8099_CR30","doi-asserted-by":"crossref","unstructured":"Stephen O et al. (2019) An efficient deep learning approach to pneumonia classification in healthcare.\u00a0J Healthcare Eng","DOI":"10.1155\/2019\/4180949"},{"key":"8099_CR31","doi-asserted-by":"crossref","unstructured":"Manickam A et al. (2021) Automated pneumonia detection on chest X-ray images: a deep learning approach with different optimizers and transfer learning architectures.\u00a0Measurement\u00a0184:109953","DOI":"10.1016\/j.measurement.2021.109953"},{"key":"8099_CR32","doi-asserted-by":"crossref","unstructured":"Nguyen H et al. (2020) Explanation of the convolutional neural network classifying chest X-ray images supporting pneumonia diagnosis. EAI Endors Trans Context Aware Syst Appl 7(21)","DOI":"10.4108\/eai.13-7-2018.165349"},{"issue":"1","key":"8099_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102411","volume":"58","author":"X Yu","year":"2021","unstructured":"Yu X, Wang S-H, Zhang Y-D (2021) CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia. Inf Process Manage 58(1):102411","journal-title":"Inf Process Manage"},{"key":"8099_CR34","doi-asserted-by":"crossref","unstructured":"Mittal A et al. (2020) Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images.\u00a0Sensors\u00a020(4):1068","DOI":"10.3390\/s20041068"},{"key":"8099_CR35","doi-asserted-by":"crossref","unstructured":"Wu H et al. (2020) Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J Intell Fuzzy Syst 39(3):2893\u20132907","DOI":"10.3233\/JIFS-191438"},{"key":"8099_CR36","doi-asserted-by":"crossref","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","DOI":"10.1109\/TKDE.2009.191"},{"key":"8099_CR37","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"8099_CR38","unstructured":"Howard AG et al.\u00a0(2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"8099_CR39","doi-asserted-by":"crossref","unstructured":"Szegedy C et al. (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"8099_CR40","doi-asserted-by":"crossref","unstructured":"Huang G et al. (2017) Densely connected convolutional networks.\u00a0In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.243"},{"key":"8099_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C et al. (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.308"},{"key":"8099_CR42","doi-asserted-by":"crossref","unstructured":"He K et al. (2016) Deep residual learning for image recognition.\u00a0In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"8099_CR43","doi-asserted-by":"crossref","unstructured":"He K et al. (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"8099_CR44","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.195"},{"issue":"2065","key":"8099_CR45","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","volume":"374","author":"IT Jolliffe","year":"2016","unstructured":"Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci 374(2065):20150202","journal-title":"Philos Trans R Soc A Math Phys Eng Sci"},{"key":"8099_CR46","unstructured":"Ezukwoke K, Zareian SJ (2019) Kernel methods for principal component analysis (PCA) A comparative study of classical and kernel PCA. A preprint\u00a0"},{"key":"8099_CR47","doi-asserted-by":"crossref","unstructured":"Rajaraman S et al. (2018) Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci 8(10):1715","DOI":"10.3390\/app8101715"},{"key":"8099_CR48","doi-asserted-by":"crossref","unstructured":"Hashmi MF et al. (2020) Efficient pneumonia detection in chest xray images using deep transfer learning. Diagnostics\u00a010(6):417","DOI":"10.3390\/diagnostics10060417"},{"issue":"4","key":"8099_CR49","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.irbm.2019.10.006","volume":"41","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M et al (2020) A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Irbm 41(4):212\u2013222","journal-title":"Irbm"},{"key":"8099_CR50","doi-asserted-by":"crossref","unstructured":"Howard A et al. (2019) Searching for mobilenetv3.\u00a0In: Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00140"},{"key":"8099_CR51","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR"},{"key":"8099_CR52","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665\u2013132676. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010287","journal-title":"IEEE Access"},{"key":"8099_CR53","unstructured":"Yang X et al.\u00a0(2020) COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:2003.13865"},{"key":"8099_CR54","doi-asserted-by":"crossref","unstructured":"Nafi\u2019iyah N, Setyati E (2021) Lung X-ray image enhancement to identify pneumonia with CNN.\u00a0In: 2021 3rd East Indonesia conference on computer and information technology (EIConCIT). IEEE","DOI":"10.1109\/EIConCIT50028.2021.9431856"},{"key":"8099_CR55","unstructured":"https:\/\/www.kaggle.com\/c\/detecting-pneumonia-using-cnn-in-pytorch\/data"},{"key":"8099_CR56","unstructured":"https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia"},{"key":"8099_CR57","doi-asserted-by":"crossref","unstructured":"Zhou B et al. (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.319"},{"key":"8099_CR58","unstructured":"Van der Maaten L, Hinton, G (2008) Visualizing data using t-SNE.\u00a0J Mach Learn Res 9(11)"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08099-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T12:15:37Z","timestamp":1679400937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08099-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":58,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["8099"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08099-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,7]]},"assertion":[{"value":"23 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"None.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"None.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}