{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T04:12:09Z","timestamp":1749701529946,"version":"3.41.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09629-8","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T14:23:25Z","timestamp":1749651805000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of chest radiographs into healthy\/pneumonia using Harris-Hawks Algorithm optimized deep-features"],"prefix":"10.1007","volume":"28","author":[{"given":"K.","family":"Vijayakumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Nazmul Hasan","family":"Maziz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swaetha","family":"Ramadasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Arunmozhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"issue":"3","key":"9629_CR1","doi-asserted-by":"publisher","first-page":"799","DOI":"10.3390\/s22030799","volume":"22","author":"F Afza","year":"2022","unstructured":"Afza F, Sharif M, Khan MA, Tariq U, Yong HS, Cha J. Multiclass skin lesion classification using hybrid deep features selection and extreme learning machine. Sensors. 2022;22(3):799.","journal-title":"Sensors"},{"issue":"4","key":"9629_CR2","first-page":"583","volume":"28","author":"Z Li","year":"2020","unstructured":"Li Z, Zeng B, Lei P, Liu J, Fan B, Shen Q, et al. Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative. J Xray Sci Technol. 2020;28(4):583\u20139.","journal-title":"J Xray Sci Technol"},{"key":"9629_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.patrec.2020.12.010","volume":"143","author":"N Dey","year":"2021","unstructured":"Dey N, Zhang YD, Rajinikanth V, Pugalenthi R, Raja NSM. Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recogn Lett. 2021;143:67\u201374.","journal-title":"Pattern Recogn Lett"},{"key":"9629_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103869","volume":"122","author":"T Mahmud","year":"2020","unstructured":"Mahmud T, Rahman MA, Fattah SA. CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med. 2020;122: 103869.","journal-title":"Comput Biol Med"},{"key":"9629_CR5","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","volume":"145","author":"AK Jaiswal","year":"2019","unstructured":"Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ. Identifying pneumonia in chest X-rays: a deep learning approach. Measurement. 2019;145:511\u20138.","journal-title":"Measurement"},{"issue":"51747","key":"9629_CR6","first-page":"51771","volume":"9","author":"W Khan","year":"2021","unstructured":"Khan W, Zaki N, Ali L. Intelligent pneumonia identification from chest x-rays: A systematic literature review. IEEE Access. 2021;9(51747):51771.","journal-title":"IEEE Access"},{"issue":"12","key":"9629_CR7","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.3390\/diagnostics11122208","volume":"11","author":"MA Khan","year":"2021","unstructured":"Khan MA, Rajinikanth V, Satapathy SC, Taniar D, Mohanty JR, Tariq U, Dama\u0161evi\u010dius R. VGG19 network assisted joint segmentation and classification of lung nodules in CT images. Diagnostics. 2021;11(12):2208.","journal-title":"Diagnostics"},{"key":"9629_CR8","first-page":"961","volume":"29","author":"MP Rajakumar","year":"2021","unstructured":"Rajakumar MP, Sonia R, Uma Maheswari B, Karuppiah SP. Tuberculosis detection in chest X-ray using Mayfly-algorithm optimized dual-deep-learning features. J X-ray Sci Technol. 2021;29:961\u201374.","journal-title":"J X-ray Sci Technol"},{"issue":"10","key":"9629_CR9","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.3390\/app8101715","volume":"8","author":"S Rajaraman","year":"2018","unstructured":"Rajaraman S, Candemir S, Kim I, Thoma G, Antani S. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci. 2018;8(10):1715.","journal-title":"Appl Sci"},{"issue":"9","key":"9629_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0256630","volume":"16","author":"R Kundu","year":"2021","unstructured":"Kundu R, Das R, Geem ZW, Han GT, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS ONE. 2021;16(9): e0256630.","journal-title":"PLoS ONE"},{"key":"9629_CR11","doi-asserted-by":"crossref","unstructured":"Ayan E, \u00dcnver HM. Diagnosis of pneumonia from chest X-ray images using deep learning. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). IEEE; 2019. p. 1\u20135","DOI":"10.1109\/EBBT.2019.8741582"},{"key":"9629_CR12","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.patrec.2019.11.013","volume":"129","author":"A Bhandary","year":"2020","unstructured":"Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, et al. Deep-learning framework to detect lung abnormality\u2013a study with chest X-Ray and lung CT scan images. Pattern Recogn Lett. 2020;129:271\u20138.","journal-title":"Pattern Recogn Lett"},{"issue":"2","key":"9629_CR13","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/app10020559","volume":"10","author":"V Chouhan","year":"2020","unstructured":"Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, et al. A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci. 2020;10(2):559.","journal-title":"Appl Sci"},{"key":"9629_CR14","doi-asserted-by":"crossref","unstructured":"Gu X, Pan L, Liang H, Yang R. Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In: Proceedings of the 3rd international conference on multimedia and image processing; 2018. p. 88\u201393.","DOI":"10.1145\/3195588.3195597"},{"key":"9629_CR15","doi-asserted-by":"crossref","unstructured":"Khatri A, Jain R, Vashista H, Mittal N, Ranjan P, Janardhanan R. Pneumonia identification in chest X-ray images using EMD. In: Trends in communication, cloud, and Big Data; 2020. p. 87\u201398.","DOI":"10.1007\/978-981-15-1624-5_9"},{"issue":"9","key":"9629_CR16","doi-asserted-by":"publisher","first-page":"3233","DOI":"10.3390\/app10093233","volume":"10","author":"T Rahman","year":"2020","unstructured":"Rahman T, Chowdhury ME, Khandakar A, Islam KR, Islam KF, Mahbub ZB, et al. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci. 2020;10(9):3233.","journal-title":"Appl Sci"},{"key":"9629_CR17","unstructured":"Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning; 2017. arXiv preprint arXiv:1711.05225."},{"key":"9629_CR18","doi-asserted-by":"crossref","unstructured":"Saraiva AA, Santos DBS, Costa NJC, Sousa JVM, Ferreira NMF, Valente A, Soares S. Models of learning to classify X-ray images for the detection of pneumonia using neural networks. In: Bioimaging; 2019. p. 76\u201383.","DOI":"10.5220\/0007346600760083"},{"issue":"4","key":"9629_CR19","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, Ergen B, C\u00f6mert Z, \u00d6zyurt F. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Irbm. 2020;41(4):212\u201322.","journal-title":"Irbm"},{"key":"9629_CR20","unstructured":"https:\/\/www.kaggle.com\/datasets\/artyomkolas\/3-kinds-of-pneumonia\/data"},{"issue":"3","key":"9629_CR21","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1177\/1063293X211025105","volume":"29","author":"K Vijayakumar","year":"2021","unstructured":"Vijayakumar K, Kadam VJ, Sharma SK. Breast cancer diagnosis using multiple activation deep neural network. Concurr Eng. 2021;29(3):275\u201384.","journal-title":"Concurr Eng"},{"key":"9629_CR22","doi-asserted-by":"crossref","unstructured":"Chen X, Wang X, Zhang K, Zhang R, Fung KM, Thai TC et al. Recent advances and clinical applications of deep learning in medical image analysis; 2021. arXiv preprint arXiv:2105.13381.","DOI":"10.1016\/j.media.2022.102444"},{"key":"9629_CR23","first-page":"1","volume":"30","author":"CS Widodo","year":"2021","unstructured":"Widodo CS, Naba A, Mahasin MM, Yueniwati Y, Putranto TA, Patra PI. UBNet: deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients. J X-ray Sci Technol. 2021;30:1\u201315.","journal-title":"J X-ray Sci Technol"},{"key":"9629_CR24","first-page":"1","volume":"29","author":"S MasoudRezaeijo","year":"2021","unstructured":"MasoudRezaeijo S, Abedi-Firouzjah R, Ghorvei M, Sarnameh S. Screening of COVID-19 based on the extracted radiomics features from chest CT images. J X-ray Sci Technol. 2021;29:1\u201315.","journal-title":"J X-ray Sci Technol"},{"key":"9629_CR25","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst. 2019;97:849\u201372.","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"9629_CR26","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10462-020-09860-3","volume":"54","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Ding W, El-Shahat D. A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev. 2021;54(1):593\u2013637.","journal-title":"Artif Intell Rev"},{"key":"9629_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107698","volume":"111","author":"R Bandyopadhyay","year":"2021","unstructured":"Bandyopadhyay R, Basu A, Cuevas E, Sarkar R. Harris Hawks optimisation with simulated annealing as a deep feature selection method for screening of COVID-19 CT-scans. Appl Soft Comput. 2021;111: 107698.","journal-title":"Appl Soft Comput"},{"key":"9629_CR28","doi-asserted-by":"publisher","first-page":"121127","DOI":"10.1109\/ACCESS.2020.3006473","volume":"8","author":"R Sihwail","year":"2020","unstructured":"Sihwail R, Omar K, Ariffin KAZ, Tubishat M. Improved harrishawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access. 2020;8:121127\u201345.","journal-title":"IEEE Access"},{"issue":"4","key":"9629_CR29","doi-asserted-by":"publisher","first-page":"3741","DOI":"10.1007\/s00366-020-01028-5","volume":"37","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Liu R, Wang X, Chen H, Li C. Boosted binary Harris hawks optimizer and feature selection. Eng Comput. 2021;37(4):3741\u201370.","journal-title":"Eng Comput"},{"key":"9629_CR30","doi-asserted-by":"crossref","unstructured":"Mirniaharikandehei S, Heidari M, Danala G, Lakshmivarahan S, Zheng B. A novel feature reduction method to improve performance of machine learning model. In: Medical imaging 2021: computer-aided diagnosis, vol. 11597. International Society for Optics and Photonics; 2021. p. 1159726.","DOI":"10.1117\/12.2580732"},{"issue":"4","key":"9629_CR31","first-page":"423","volume":"19","author":"N Parveen","year":"2011","unstructured":"Parveen N, Sathik MM. Detection of pneumonia in chest X-ray images. J Xray Sci Technol. 2011;19(4):423\u20138.","journal-title":"J Xray Sci Technol"},{"key":"9629_CR32","doi-asserted-by":"crossref","unstructured":"Chandra TB, Verma K. Pneumonia detection on chest x-ray using machine learning paradigm. In: Proceedings of 3rd international conference on computer vision and image processing: CVIP 2018, vol 1. Springer Singapore; 2020. p. 21\u201333","DOI":"10.1007\/978-981-32-9088-4_3"},{"key":"9629_CR33","doi-asserted-by":"crossref","unstructured":"Arunmozhi S, Rajinikanth V, Rajakumar MP. Deep-learning based automated detection of pneumonia in chest radiographs. In: 2021 International conference on system, computation, automation and networking (ICSCAN). IEEE; 2021. p. 1\u20134.","DOI":"10.1109\/ICSCAN53069.2021.9526482"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09629-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09629-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09629-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T14:23:29Z","timestamp":1749651809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09629-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9629"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09629-8","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]},"assertion":[{"value":"30 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"115"}}