{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:28:22Z","timestamp":1774380502214,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01201-y","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T16:02:16Z","timestamp":1723564936000},"page":"727-746","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1382-1141","authenticated-orcid":false,"given":"Rupali","family":"Vyas","sequence":"first","affiliation":[]},{"given":"Deepak Rao","family":"Khadatkar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"issue":"1","key":"1201_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41572-021-00259-0","volume":"7","author":"A Torres","year":"2021","unstructured":"A. Torres et al., \u201cPneumonia,\u201d Nat Rev Dis Primers, vol. 7, no. 1, p. 25, Apr. 2021,\u00a0https:\/\/doi.org\/10.1038\/s41572-021-00259-0.","journal-title":"Nat Rev Dis Primers"},{"key":"1201_CR2","doi-asserted-by":"publisher","unstructured":"S. Rawat et al., \u201cIntroduction to Lung Disease,\u201d in Natural Polymeric Materials based Drug Delivery Systems in Lung Diseases, Singapore: Springer Nature Singapore, 2023, pp. 1\u201324. https:\/\/doi.org\/10.1007\/978-981-19-7656-8_1.","DOI":"10.1007\/978-981-19-7656-8_1"},{"issue":"5","key":"1201_CR3","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2019-001715","volume":"4","author":"L Macpherson","year":"2019","unstructured":"L. Macpherson et al., \u201cRisk factors for death among children aged 5\u201314 years hospitalised with pneumonia: a retrospective cohort study in Kenya,\u201d BMJ Glob Health, vol. 4, no. 5, p. e001715, Sep. 2019, https:\/\/doi.org\/10.1136\/bmjgh-2019-001715.","journal-title":"BMJ Glob Health"},{"key":"1201_CR4","doi-asserted-by":"publisher","unstructured":"M. AL-Muzahmi, M. Rizvi, M. AL-Quraini, Z. AL-Muharrmi, and Z. AL-Jabri, \u201cComparative Genomic Analysis Reveals the Emergence of ST-231 and ST-395 Klebsiella pneumoniae Strains Associated with the High Transmissibility of blaKPC Plasmids,\u201d Microorganisms, vol. 11, no. 10, Oct. 2023, https:\/\/doi.org\/10.3390\/MICROORGANISMS11102411.","DOI":"10.3390\/MICROORGANISMS11102411"},{"issue":"5","key":"1201_CR5","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1097\/RTI.0000000000000347","volume":"33","author":"T Franquet","year":"2018","unstructured":"T. Franquet, \u201cImaging of Community-Acquired Pneumonia,\u201d J Thorac Imaging, vol. 33, no. 5, pp. 282\u2013294, Sep. 2018, https:\/\/doi.org\/10.1097\/RTI.0000000000000347.","journal-title":"J Thorac Imaging"},{"issue":"4","key":"1201_CR6","doi-asserted-by":"publisher","first-page":"177","DOI":"10.18201\/ijisae.2020466310","volume":"8","author":"MB Darici","year":"2020","unstructured":"M. B. Darici, Z. Dokur, and T. Olmez, \u201cPneumonia Detection and Classification Using Deep Learning on Chest X-Ray Images,\u201d International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 4, pp. 177\u2013183, Dec. 2020,\u00a0https:\/\/doi.org\/10.18201\/ijisae.2020466310.","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"issue":"3","key":"1201_CR7","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/J.POP.2018.04.001","volume":"45","author":"SN Grief","year":"2018","unstructured":"S. N. Grief and J. K. Loza, \u201cGuidelines for the Evaluation and Treatment of Pneumonia,\u201d Prim Care, vol. 45, no. 3, pp. 485\u2013503, Sep. 2018,\u00a0https:\/\/doi.org\/10.1016\/J.POP.2018.04.001.","journal-title":"Prim Care"},{"issue":"8","key":"1201_CR8","doi-asserted-by":"publisher","first-page":"942","DOI":"10.5005\/jp\/books\/10485_24","volume":"7","author":"SBA Sattar","year":"2023","unstructured":"S. B. A. Sattar and S. Sharma, \u201cBacterial Pneumonia,\u201d Res J Pharm Technol, vol. 7, no. 8, pp. 942\u2013945, Aug. 2023, https:\/\/doi.org\/10.5005\/jp\/books\/10485_24.","journal-title":"Res J Pharm Technol"},{"issue":"6","key":"1201_CR9","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1165\/rcmb.2020-0241OC","volume":"63","author":"S Wali","year":"2020","unstructured":"S. Wali et al., \u201cImmune Modulation to Improve Survival of Viral Pneumonia in Mice,\u201d Am J Respir Cell Mol Biol, vol. 63, no. 6, pp. 758\u2013766, Dec. 2020,\u00a0https:\/\/doi.org\/10.1165\/rcmb.2020-0241OC.","journal-title":"Am J Respir Cell Mol Biol"},{"key":"1201_CR10","doi-asserted-by":"publisher","unstructured":"S. Sajed, A. Sanati, J. E. Garcia, H. Rostami, A. Keshavarz, and A. Teixeira, \u201cThe effectiveness of deep learning vs. traditional methods for lung disease diagnosis using chest X-ray images: A systematic review,\u201d Applied Soft Computing, vol. 147. Elsevier Ltd, Nov. 01, 2023. https:\/\/doi.org\/10.1016\/j.asoc.2023.110817.","DOI":"10.1016\/j.asoc.2023.110817"},{"key":"1201_CR11","doi-asserted-by":"publisher","unstructured":"I. Lahsaini, M. El Habib Daho, and M. A. Chikh, \u201cConvolutional Neural Network for Chest X-ray Pneumonia Detection,\u201d ACM International Conference Proceeding Series, Oct. 2020, https:\/\/doi.org\/10.1145\/3432867.3432873.","DOI":"10.1145\/3432867.3432873"},{"key":"1201_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/J.MEDIA.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"E. \u00c7all\u0131, E. Sogancioglu, B. van Ginneken, K. G. van Leeuwen, and K. Murphy, \u201cDeep learning for chest X-ray analysis: A survey,\u201d Med Image Anal, vol. 72, p. 102125, Aug. 2021,\u00a0https:\/\/doi.org\/10.1016\/J.MEDIA.2021.102125.","journal-title":"Med Image Anal"},{"key":"1201_CR13","doi-asserted-by":"publisher","unstructured":"Z. Jiang, \u201cChest X-ray Pneumonia Detection Based on Convolutional Neural Networks,\u201d Proceedings - 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020, pp. 341\u2013344, Jun. 2020, https:\/\/doi.org\/10.1109\/ICBAIE49996.2020.00077.","DOI":"10.1109\/ICBAIE49996.2020.00077"},{"key":"1201_CR14","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","volume":"145","author":"AK Jaiswal","year":"2019","unstructured":"A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, \u201cIdentifying pneumonia in chest X-rays: A deep learning approach,\u201d Measurement (Lond), vol. 145, pp. 511\u2013518, Oct. 2019,\u00a0https:\/\/doi.org\/10.1016\/j.measurement.2019.05.076.","journal-title":"Measurement (Lond)"},{"key":"1201_CR15","doi-asserted-by":"publisher","unstructured":"S. L. K. Yee and W. J. K. Raymond, \u201cPneumonia Diagnosis Using Chest X-ray Images and Machine Learning,\u201d ACM International Conference Proceeding Series, pp. 101\u2013105, Sep. 2020, https:\/\/doi.org\/10.1145\/3397391.3397412.","DOI":"10.1145\/3397391.3397412"},{"key":"1201_CR16","doi-asserted-by":"publisher","unstructured":"A. Tilve, S. Nayak, S. Vernekar, D. Turi, P. R. Shetgaonkar, and S. Aswale, \u201cPneumonia Detection Using Deep Learning Approaches,\u201d International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020, Feb. 2020, https:\/\/doi.org\/10.1109\/IC-ETITE47903.2020.152.","DOI":"10.1109\/IC-ETITE47903.2020.152"},{"key":"1201_CR17","doi-asserted-by":"publisher","unstructured":"D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, \u201cPneumonia Detection Using CNN based Feature Extraction,\u201d Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, Feb. 2019, https:\/\/doi.org\/10.1109\/ICECCT.2019.8869364.","DOI":"10.1109\/ICECCT.2019.8869364"},{"key":"1201_CR18","doi-asserted-by":"publisher","unstructured":"F. T. Porras, C. Rodriguez, D. Rodriguez, P. Lezama, R. Inquilla, and Y. Pomachagua, \u201cDeep Learning Algorithms in Chest Images for Pneumonia Detection,\u201d Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, pp. 442\u2013447, 2022, https:\/\/doi.org\/10.1109\/CICN56167.2022.10008321.","DOI":"10.1109\/CICN56167.2022.10008321"},{"key":"1201_CR19","doi-asserted-by":"publisher","unstructured":"Y. Muhammad, M. D. Alshehri, W. M. Alenazy, T. Vinh Hoang, and R. Alturki, \u201cIdentification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques,\u201d Mobile Information Systems, vol. 2021, 2021, https:\/\/doi.org\/10.1155\/2021\/9989237.","DOI":"10.1155\/2021\/9989237"},{"issue":"1","key":"1201_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/S13735-021-00204-7\/TABLES\/6","volume":"10","author":"K El Asnaoui","year":"2021","unstructured":"K. El Asnaoui, \u201cDesign ensemble deep learning model for pneumonia disease classification,\u201d Int J Multimed Inf Retr, vol. 10, no. 1, pp. 55\u201368, Mar. 2021,\u00a0https:\/\/doi.org\/10.1007\/S13735-021-00204-7\/TABLES\/6.","journal-title":"Int J Multimed Inf Retr"},{"key":"1201_CR21","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.patrec.2020.12.010","volume":"143","author":"N Dey","year":"2021","unstructured":"N. Dey, Y. D. Zhang, V. Rajinikanth, R. Pugalenthi, and N. S. M. Raja, \u201cCustomized VGG19 Architecture for Pneumonia Detection in Chest X-Rays,\u201d Pattern Recognit Lett, vol. 143, pp. 67\u201374, Mar. 2021, https:\/\/doi.org\/10.1016\/j.patrec.2020.12.010.","journal-title":"Pattern Recognit Lett"},{"key":"1201_CR22","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/j.aej.2022.10.053","volume":"64","author":"GMM Alshmrani","year":"2023","unstructured":"G. M. M. Alshmrani, Q. Ni, R. Jiang, H. Pervaiz, and N. M. Elshennawy, \u201cA deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images,\u201d Alexandria Engineering Journal, vol. 64, pp. 923\u2013935, Feb. 2023,\u00a0https:\/\/doi.org\/10.1016\/j.aej.2022.10.053.","journal-title":"Alexandria Engineering Journal"},{"key":"1201_CR23","doi-asserted-by":"publisher","unstructured":"V. Rajinikanth, S. Kadry, R. Damasevicius, C. Pandeeswaran, M. Abed Mohammed, and G. Glan Devadhas, \u201cPneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification,\u201d Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies: Computational Intelligence for Smart Systems, ICICICT 2022, pp. 972\u2013976, 2022, https:\/\/doi.org\/10.1109\/ICICICT54557.2022.9917698.","DOI":"10.1109\/ICICICT54557.2022.9917698"},{"key":"1201_CR24","doi-asserted-by":"publisher","unstructured":"D. Kikoo, B. Tamin, S. Hardjadilaga, - Anderies, and I. A. Iswanto, \u201cUsing Various Convolutional Neural Network to Detect Pneumonia from Chest X-Ray Images: A Systematic Literature Review,\u201d JOIV\u00a0: International Journal on Informatics Visualization, vol. 7, no. 2, p. 310, May 2023, https:\/\/doi.org\/10.30630\/joiv.7.2.1015.","DOI":"10.30630\/joiv.7.2.1015"},{"key":"1201_CR25","doi-asserted-by":"publisher","unstructured":"R. E. Al Mamlook, S. Chen, and H. F. Bzizi, \u201cInvestigation of the performance of Machine Learning Classifiers for Pneumonia Detection in Chest X-ray Images,\u201d IEEE International Conference on Electro Information Technology, vol. 2020-July, pp. 98\u2013104, Jul. 2020, https:\/\/doi.org\/10.1109\/EIT48999.2020.9208232.","DOI":"10.1109\/EIT48999.2020.9208232"},{"key":"1201_CR26","doi-asserted-by":"publisher","unstructured":"D. Kermany, K. Zhang, and M. Goldbaum, \u201cLabeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,\u201d vol. 2, 2018, https:\/\/doi.org\/10.17632\/RSCBJBR9SJ.2.","DOI":"10.17632\/RSCBJBR9SJ.2"},{"key":"1201_CR27","doi-asserted-by":"publisher","unstructured":"S. Anand, R. K. Roshan, and D. Sundaram M, \u201cChest X ray image enhancement using deep contrast diffusion learning,\u201d Optik (Stuttg), vol. 279, p. 170751, May 2023, https:\/\/doi.org\/10.1016\/J.IJLEO.2023.170751.","DOI":"10.1016\/J.IJLEO.2023.170751"},{"issue":"4","key":"1201_CR28","doi-asserted-by":"publisher","first-page":"3239","DOI":"10.1007\/S12652-021-03464-7\/TABLES\/17","volume":"14","author":"S Goyal","year":"2023","unstructured":"S. Goyal and R. Singh, \u201cDetection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques,\u201d J Ambient Intell Humaniz Comput, vol. 14, no. 4, pp. 3239\u20133259, Apr. 2023, https:\/\/doi.org\/10.1007\/S12652-021-03464-7\/TABLES\/17.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1201_CR29","doi-asserted-by":"publisher","unstructured":"G. Shih et al., \u201cAugmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia,\u201d Radiol Artif Intell, vol. 1, no. 1, Jan. 2019, https:\/\/doi.org\/10.1148\/RYAI.2019180041\/ASSET\/IMAGES\/LARGE\/RYAI.2019180041.FIG3.JPEG.","DOI":"10.1148\/RYAI.2019180041\/ASSET\/IMAGES\/LARGE\/RYAI.2019180041.FIG3.JPEG"},{"key":"1201_CR30","doi-asserted-by":"publisher","unstructured":"J. Garstka and M. Strzelecki, \u201cPneumonia detection in X-ray chest images based on convolutional neural networks and data augmentation methods,\u201d Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA, vol. 2020-September, pp. 18\u201323, Sep. 2020, https:\/\/doi.org\/10.23919\/SPA50552.2020.9241305.","DOI":"10.23919\/SPA50552.2020.9241305"},{"key":"1201_CR31","doi-asserted-by":"publisher","unstructured":"N. Patil, K. Ingole, and T. Rajani Mangala, \u201cDeep Convolutional Neural Networks Approach for Classification of Lung Diseases using X-Rays: COVID-19, Pneumonia, and Tuberculosis,\u201d International Journal of Performability Engineering, vol. 16, no. 9, p. 1332, Sep. 2020, https:\/\/doi.org\/10.23940\/IJPE.20.09.P2.13321340.","DOI":"10.23940\/IJPE.20.09.P2.13321340"},{"issue":"9","key":"1201_CR32","doi-asserted-by":"publisher","first-page":"544","DOI":"10.14569\/IJACSA.2022.0130963","volume":"13","author":"O Iparraguirre-Villanueva","year":"2022","unstructured":"O. Iparraguirre-Villanueva, V. Guevara-Ponce, O. R. Paredes, F. Sierra-Li\u00f1an, J. Zapata-Paulini, and M. Cabanillas-Carbonell, \u201cConvolutional Neural Networks with Transfer Learning for Pneumonia Detection,\u201d International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, pp. 544\u2013551, 2022,\u00a0https:\/\/doi.org\/10.14569\/IJACSA.2022.0130963.","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"1201_CR33","doi-asserted-by":"publisher","unstructured":"G. Ali, A. Shahin, M. Elhadidi, and M. Elattar, \u201cConvolutional Neural Network with Attention Modules for Pneumonia Detection,\u201d 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020, Dec. 2020, https:\/\/doi.org\/10.1109\/3ICT51146.2020.9311985.","DOI":"10.1109\/3ICT51146.2020.9311985"},{"issue":"3","key":"1201_CR34","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.11591\/IJEECS.V24.I3.PP1469-1480","volume":"24","author":"O Dahmane","year":"2021","unstructured":"O. Dahmane, M. Khelifi, M. Beladgham, and I. Kadri, \u201cPneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch,\u201d Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 3, pp. 1469\u20131480, Dec. 2021, https:\/\/doi.org\/10.11591\/IJEECS.V24.I3.PP1469-1480.","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"1201_CR35","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https:\/\/arxiv.org\/abs\/1409.1556v6"},{"key":"1201_CR36","doi-asserted-by":"publisher","unstructured":"P. Naveen and B. Diwan, \u201cPre-trained VGG-16 with CNN architecture to classify X-Rays images into normal or pneumonia,\u201d 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, pp. 102\u2013105, Mar. 2021, https:\/\/doi.org\/10.1109\/ESCI50559.2021.9396997.","DOI":"10.1109\/ESCI50559.2021.9396997"},{"key":"1201_CR37","doi-asserted-by":"publisher","unstructured":"A. \u00c7inar, M. Yildirim, Y. Ero\u011flu, A. \u00c7\u0131nar, and M. Y\u0131ld\u0131r\u0131m, \u201cClassification of Pneumonia Cell Images Using Improved ResNet50 Model\u201d, https:\/\/doi.org\/10.18280\/ts.380117.","DOI":"10.18280\/ts.380117"},{"key":"1201_CR38","doi-asserted-by":"publisher","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770\u2013778, Dec. 2016, https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1201_CR39","doi-asserted-by":"publisher","unstructured":"M. Rahimzadeh and A. Attar, \u201cA modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2,\u201d Inform Med Unlocked, vol. 19, Jan. 2020, https:\/\/doi.org\/10.1016\/j.imu.2020.100360.","DOI":"10.1016\/j.imu.2020.100360"},{"key":"1201_CR40","doi-asserted-by":"publisher","unstructured":"T. A. Youssef, B. Aissam, D. Khalid, B. Imane, and J. El Miloud, \u201cClassification of chest pneumonia from x-ray images using new architecture based on ResNet,\u201d 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020, Dec. 2020, https:\/\/doi.org\/10.1109\/ICECOCS50124.2020.9314567.","DOI":"10.1109\/ICECOCS50124.2020.9314567"},{"key":"1201_CR41","doi-asserted-by":"publisher","unstructured":"M. F. Hashmi, S. Katiyar, A. G. Keskar, N. D. Bokde, and Z. W. Geem, \u201cEfficient pneumonia detection in chest xray images using deep transfer learning,\u201d Diagnostics, vol. 10, no. 6, 2020, https:\/\/doi.org\/10.3390\/diagnostics10060417.","DOI":"10.3390\/diagnostics10060417"},{"key":"1201_CR42","doi-asserted-by":"publisher","unstructured":"S. Pappula, T. Nadendla, N. B. Lomadugu, and S. Revanth Nalla, \u201cDetection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model,\u201d 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, pp. 1671\u20131675, 2023, https:\/\/doi.org\/10.1109\/ICACCS57279.2023.10113110.","DOI":"10.1109\/ICACCS57279.2023.10113110"},{"key":"1201_CR43","doi-asserted-by":"publisher","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, \u201cDensely connected convolutional networks,\u201d Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 2261\u20132269, Nov. 2017, https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1201_CR44","doi-asserted-by":"publisher","unstructured":"I. F. Jassam, S. M. Elkaffas, and A. A. El-Zoghabi, \u201cChest X-Ray Pneumonia Detection by Dense-Net,\u201d in 2021 31st International Conference on Computer Theory and Applications (ICCTA), IEEE, Dec. 2021, pp. 176\u2013179. https:\/\/doi.org\/10.1109\/ICCTA54562.2021.9916637.","DOI":"10.1109\/ICCTA54562.2021.9916637"},{"key":"1201_CR45","doi-asserted-by":"publisher","unstructured":"G. Labhane, R. Pansare, S. Maheshwari, R. Tiwari, and A. Shukla, \u201cDetection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning,\u201d Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020, pp. 85\u201392, Feb. 2020, https:\/\/doi.org\/10.1109\/ICETCE48199.2020.9091755.","DOI":"10.1109\/ICETCE48199.2020.9091755"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01201-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01201-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01201-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:18:14Z","timestamp":1743344294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01201-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1201"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01201-y","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"9 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 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":"Not applicable. This research did not involve human participants, their data, or biological material. The study was conducted using publicly available data, which was cited accordingly in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable. This research involved no human subjects or personal data, as the study was based on analysis of publicly available datasets.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable. This manuscript does not contain any individual person\u2019s data in any form.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}