{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:17:06Z","timestamp":1773155826740,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18975-6","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T07:02:06Z","timestamp":1712300526000},"page":"5491-5521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Pneumonia detection in chest x-ray images using an optimized ensemble with XGBoost classifier"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1961-4032","authenticated-orcid":false,"given":"Mohammed","family":"El-Ghandour","sequence":"first","affiliation":[]},{"given":"Marwa Ismael","family":"Obayya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"18975_CR1","doi-asserted-by":"crossref","first-page":"P1","DOI":"10.1164\/rccm.1931P1","volume":"193","author":"American Thoracic Society","year":"2016","unstructured":"American Thoracic Society (2016) Wath is pneumonia. Am J Respir Crit Care Med 193:P1\u2013P2.https:\/\/www.thoracic.org\/patients\/patient-resources\/resources\/what-is-pneumonia.pdf","journal-title":"Am J Respir Crit Care Med"},{"key":"18975_CR2","unstructured":"https:\/\/ourworldindata.org\/grapher\/pneumonia-and-lower-respiratory-diseases-deaths. Accessed 27 Jul 2022"},{"issue":"2","key":"18975_CR3","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ajem.2012.08.041","volume":"31","author":"WH Self","year":"2013","unstructured":"Self WH, Courtney DM, McNaughton CD, Wunderink RG, Kline JA (2013) High discordance of chest x-ray and computed tomography for detection of pulmonary opacities in ed patients: implications for diagnosing pneumonia. Am J Emerg Med 31(2):401\u2013405","journal-title":"Am J Emerg Med"},{"issue":"4","key":"18975_CR4","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1111\/j.1526-4610.2008.01071.x","volume":"48","author":"SJ Tepper","year":"2008","unstructured":"Tepper SJ (2008) Computed tomography - an increasing source of radiation exposure: commentary. Headache 48(4):657. https:\/\/doi.org\/10.1111\/j.1526-4610.2008.01071.x","journal-title":"Headache"},{"key":"18975_CR5","doi-asserted-by":"publisher","unstructured":"Ticinesi A et al (2016) Lung ultrasound and chest x-ray for detecting pneumonia in an acute geriatric ward. Medicine (United States) 95(27). https:\/\/doi.org\/10.1097\/MD.0000000000004153","DOI":"10.1097\/MD.0000000000004153"},{"key":"18975_CR6","doi-asserted-by":"publisher","unstructured":"Ayan E, \u00dcnver HM (2019) Diagnosis of pneumonia from chest X-ray images using deep learning. Sci Meet Electr Biomed Eng Comput Sci EBBT, pp 0\u20134. https:\/\/doi.org\/10.1109\/EBBT.2019.8741582","DOI":"10.1109\/EBBT.2019.8741582"},{"key":"18975_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103182","volume":"71","author":"A Bhattacharyya","year":"2022","unstructured":"Bhattacharyya A, Bhaik D, Kumar S, Thakur P, Sharma R, Pachori RB (2022) A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomed Signal Process Control 71:103182. https:\/\/doi.org\/10.1016\/j.bspc.2021.103182","journal-title":"Biomed Signal Process Control"},{"key":"18975_CR8","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 3462\u20133471","DOI":"10.1109\/CVPR.2017.369"},{"issue":"3","key":"18975_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1038\/s41571-019-0323-0","volume":"17","author":"D Romero","year":"2020","unstructured":"Romero D (2020) APBI is an alternative to WBI. Nat Rev Clin Oncol 17(3):134. https:\/\/doi.org\/10.1038\/s41571-019-0323-0","journal-title":"Nat Rev Clin Oncol"},{"issue":"5","key":"18975_CR10","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299\u20131312","journal-title":"IEEE Trans Med Imag"},{"issue":"1","key":"18975_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big Data"},{"key":"18975_CR12","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale Image Recognition. arXiv [cs.CV]"},{"key":"18975_CR13","unstructured":"Krizhevsky A, Ilya S, Geoffrey EH (2012) ImageNet classification with deep convolutional neural networks. In: Proc Adv Neural Inf Process Syst, pp 1097\u20131105"},{"key":"18975_CR14","doi-asserted-by":"publisher","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"18975_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, vol 2016-December, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"18975_CR16","unstructured":"Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K (2017) Learning to diagnose from scratch by exploiting dependencies among labels. arXiv:1710.10501. [Online]. Available: http:\/\/arxiv.org\/abs\/1710.10501. Accessed 17 Jul 2022"},{"key":"#cr-split#-18975_CR17.1","doi-asserted-by":"crossref","unstructured":"Rajpurkar P et al (2018) Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 15(11):e1002686. [Online]. Available:","DOI":"10.1371\/journal.pmed.1002686"},{"key":"#cr-split#-18975_CR17.2","unstructured":"https:\/\/pubmed.ncbi.nlm.nih.gov\/30457988\/, http:\/\/medicine.plosjournals.org\/perlserv\/?request=index-html&issn=1549-1676.\u00a0Accessed 17 Jul 2022"},{"key":"18975_CR18","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L, Li K, Li F-F (2009) ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18975_CR19","doi-asserted-by":"publisher","unstructured":"Stephen O, Sain M, Maduh UJ, Jeong DU (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019. https:\/\/doi.org\/10.1155\/2019\/4180949","DOI":"10.1155\/2019\/4180949"},{"key":"18975_CR20","unstructured":"Kermany D, Zhang K, Goldbaum M (2018) Labeled Optical 809 Coherence Tomography (OCT) and Chest X-Ray Images 810 for Classification, Mendeley Data : Version 2, 2018. [Online]. Available: https:\/\/www.kaggle.com\/datasets\/paultimothymooney\/chest-xray-pneumonia. Accessed 15 July 2022"},{"key":"18975_CR21","doi-asserted-by":"publisher","unstructured":"Saraiva AA et al (2019) Models of learning to classify X-ray images for the detection of pneumonia using neural networks. BIOIMAGING 2019\u20136th Int Conf Bioimaging, Proceedings; Part 12th Int Jt Conf Biomed Eng Syst Technol BIOSTEC 2019, no. Biostec, pp 76\u201383. https:\/\/doi.org\/10.5220\/0007346600760083","DOI":"10.5220\/0007346600760083"},{"key":"18975_CR22","doi-asserted-by":"publisher","first-page":"108046","DOI":"10.1016\/j.measurement.2020.108046","volume":"165","author":"R Jain","year":"2020","unstructured":"Jain R, Nagrath P, Kataria G, Kaushik VS, Jude Hemanth D (2020) Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Meas J Int Meas Confed 165:108046. https:\/\/doi.org\/10.1016\/j.measurement.2020.108046","journal-title":"Meas J Int Meas Confed"},{"issue":"3","key":"18975_CR23","doi-asserted-by":"publisher","first-page":"2893","DOI":"10.3233\/JIFS-191438","volume":"39","author":"H Wu","year":"2020","unstructured":"Wu H, Xie P, Zhang H, Li D, Cheng M (2020) Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J Intell Fuzzy Syst 39(3):2893\u20132907. https:\/\/doi.org\/10.3233\/JIFS-191438","journal-title":"J Intell Fuzzy Syst"},{"key":"18975_CR24","first-page":"1","volume":"3233","author":"T Rahman","year":"2020","unstructured":"Rahman T, Chowdhury MEH, Khandakar A (2020) Transfer learning with deep convolutional neural network (CNN) for Pneumonia Detection using chest X-ray. MDPI J App Sci 3233:1\u201317","journal-title":"MDPI J App Sci"},{"issue":"1121","key":"18975_CR25","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20201263","volume":"94","author":"M Salehi","year":"2021","unstructured":"Salehi M, Mohammadi R, Ghaffari H, Sadighi N, Reiazi R (2021) Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images. Br J Radiol 94(1121):20201263. https:\/\/doi.org\/10.1259\/bjr.20201263","journal-title":"Br J Radiol"},{"key":"18975_CR26","doi-asserted-by":"publisher","unstructured":"Zhang D, Ren F, Li Y, Na L, Ma Y (2021) Pneumonia detection from chest X-ray images based on convolutional neural network. Electron 10(13). https:\/\/doi.org\/10.3390\/electronics10131512","DOI":"10.3390\/electronics10131512"},{"issue":"6","key":"18975_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics10060417","volume":"10","author":"MF Hashmi","year":"2020","unstructured":"Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW (2020) Efficient pneumonia detection in chest xray images using deep transfer learning. Diagnostics 10(6):1\u201323. https:\/\/doi.org\/10.3390\/diagnostics10060417","journal-title":"Diagnostics"},{"issue":"13","key":"18975_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/app12136448","volume":"12","author":"A Mabrouk","year":"2022","unstructured":"Mabrouk A, D\u00edaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M (2022) Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks. Appl Sci 12(13):6448. https:\/\/doi.org\/10.3390\/app12136448","journal-title":"Appl Sci"},{"key":"18975_CR29","doi-asserted-by":"publisher","first-page":"62110","DOI":"10.1109\/ACCESS.2022.3182498","volume":"10","author":"M Yaseliani","year":"2022","unstructured":"Yaseliani M, Hamadani AZ, Maghsoodi AI, Mosavi A (2022) Pneumonia detection proposing a hybrid deep convolutional neural network based on two parallel visual geometry group architectures and machine learning classifiers. IEEE Access 10:62110\u201362128. https:\/\/doi.org\/10.1109\/ACCESS.2022.3182498","journal-title":"IEEE Access"},{"key":"18975_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13486-8","author":"A Sharma","year":"2022","unstructured":"Sharma A, Mishra PK (2022) Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-022-13486-8","journal-title":"Multimed Tools Appl"},{"key":"18975_CR31","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 2951\u20132959. https:\/\/papers.nips.cc\/paper_files\/paper\/2012\/file\/05311655a15b75fab86956663e1819cd-Paper.pdf"},{"key":"18975_CR32","unstructured":"Frazier PI (2018) A tutorial on Bayesian optimization. arXiv:1807.02811. [Online]. Available: http:\/\/arxiv.org\/abs\/1807.02811. Accessed 15 Aug 2022"},{"key":"18975_CR33","unstructured":"Mockus J (1977) On Bayesian methods for seeking the extremum. In: Proc Optim Techn IFIP Tech Conf, pp 195\u2013200"},{"issue":"4","key":"18975_CR34","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455\u2013492. https:\/\/doi.org\/10.1023\/A:1008306431147","journal-title":"J Glob Optim"},{"key":"18975_CR35","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNetV2: inverted residuals and linear bottlenecks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, pp 4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"18975_CR36","unstructured":"Howard AG et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications, [Online]. Available: http:\/\/arxiv.org\/abs\/1704.04861. Accessed 25 Sept 2022"},{"key":"18975_CR37","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, pp 6848\u20136856. https:\/\/doi.org\/10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"issue":"5","key":"18975_CR38","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189\u20131232. https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann Stat"},{"key":"18975_CR39","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proc 22nd ACM SIGKDD Int Conf Knowl Discovery Data Mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"18975_CR40","unstructured":"Tseng G. Gradient Boosting and XGBoost. Medium.com. https:\/\/medium.com\/@gabrieltseng\/gradient-boosting-andXGBoost-c306c1bcfaf5. Accessed 19 Oct 2022"},{"key":"18975_CR41","unstructured":"Laurae (n.d.) XGBoost: hi I'm gamma. What can I do for you? - and the tuning of regularization. Medium.com. https:\/\/medium.com\/data-design\/XGBoost-hi-im-gamma-what-can-i-do-for-you-and-the-tuning-of-regularization-a42ea17e6ab6. Accessed19 Oct 2022"},{"key":"18975_CR42","unstructured":"Martins D. XGBoost: a complete guide to fine-tune and optimize your model. Medium.com. https:\/\/towardsdatascience.com\/xgboost-fine-tune-and-optimize-your-model-23d996fab663. Accessed 19 Oct 2022"},{"key":"18975_CR43","doi-asserted-by":"publisher","unstructured":"Al Reshan MS et al (2023) Detection of pneumonia from chest X-ray images utilizing MobileNet model. Healthcare 11(11). https:\/\/doi.org\/10.3390\/healthcare11111561","DOI":"10.3390\/healthcare11111561"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18975-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18975-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18975-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:21:11Z","timestamp":1742689271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18975-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["18975"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18975-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,5]]},"assertion":[{"value":"17 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study does not involve any ethical issues.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}