{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T20:46:43Z","timestamp":1776718003430,"version":"3.51.2"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"41","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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\/s11042-025-21105-5","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T08:35:10Z","timestamp":1756715710000},"page":"49271-49295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An accurate attention based method for multi-tasking X-ray classification"],"prefix":"10.1007","volume":"84","author":[{"given":"Zahra","family":"Raeisi","sequence":"first","affiliation":[]},{"given":"Shayan","family":"Rokhva","sequence":"additional","affiliation":[]},{"given":"Amirsadegh","family":"Roshanzamir","sequence":"additional","affiliation":[]},{"given":"Reza","family":"ahmadi lashaki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"issue":"4","key":"21105_CR1","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/0002-9343(94)90060-4","volume":"96","author":"I Koivula","year":"1994","unstructured":"Koivula I, Sten M, Makela PH (1994) Risk factors for pneumonia in the elderly. Am J Med 96(4):313\u2013320","journal-title":"Am J Med"},{"key":"21105_CR2","doi-asserted-by":"crossref","unstructured":"Rathi R, Balayan N, Kumar CV (2020) Pneumonia detection using chest x-ray. International Journal of Pharmaceutical Research (09752366) 12(3)","DOI":"10.31838\/ijpr\/2020.12.03.181"},{"key":"21105_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.compbiomed.2017.10.011","volume":"91","author":"A Singh","year":"2017","unstructured":"Singh A, Dutta MK, Jennane R, Lespessailles E (2017) Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput Biol Med 91:148\u2013158","journal-title":"Comput Biol Med"},{"issue":"9","key":"21105_CR4","doi-asserted-by":"publisher","first-page":"6247","DOI":"10.1007\/s00330-022-08764-w","volume":"32","author":"CE von Schacky","year":"2022","unstructured":"von Schacky CE, Wilhelm NJ, Sch\u00e4fer VS, Leonhardt Y, Jung M, Jungmann PM, Russe MF, Foreman SC, Gassert FG, Gassert FT et al (2022) Development and evaluation of machine learning models based on x-ray radiomics for the classification and differentiation of malignant and benign bone tumors. Eur Radiol 32(9):6247\u20136257","journal-title":"Eur Radiol"},{"key":"21105_CR5","doi-asserted-by":"crossref","unstructured":"Kumar A, Gill AS, Singh JP, Ghosh D (2024) A comprehensive and comparative examination of machine learning techniques for diabetes mellitus prediction. In: 2024 15th International conference on computing communication and networking technologies (ICCCNT), pp 1\u20135, IEEE","DOI":"10.1109\/ICCCNT61001.2024.10725693"},{"issue":"4","key":"21105_CR6","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s12559-020-09787-5","volume":"16","author":"AU Ibrahim","year":"2024","unstructured":"Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS (2024) Pneumonia classification using deep learning from chest x-ray images during covid-19. Cogn Comput 16(4):1589\u20131601","journal-title":"Cogn Comput"},{"issue":"8","key":"21105_CR7","doi-asserted-by":"publisher","first-page":"176","DOI":"10.3390\/jimaging10080176","volume":"10","author":"R Siddiqi","year":"2024","unstructured":"Siddiqi R, Javaid S (2024) Deep learning for pneumonia detection in chest x-ray images: A comprehensive survey. Journal of Imaging 10(8):176","journal-title":"Journal of Imaging"},{"key":"21105_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2023.113924","volume":"301","author":"Y Ma","year":"2024","unstructured":"Ma Y, Chen S, Ermon S, Lobell DB (2024) Transfer learning in environmental remote sensing. Remote Sens Environ 301:113924","journal-title":"Remote Sens Environ"},{"key":"21105_CR9","doi-asserted-by":"crossref","unstructured":"B. Koonce, B. Koonce (2021) Resnet 50. Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization, pp 63\u201372","DOI":"10.1007\/978-1-4842-6168-2_6"},{"key":"21105_CR10","unstructured":"K. Simonyan, A. Zisserman (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"21105_CR11","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K. Weinberger (2016) Densely connected convolutional networks. cvpr 2017. arxiv. arXiv:1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"21105_CR12","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.gltp.2021.08.027","volume":"2","author":"AV Ikechukwu","year":"2021","unstructured":"Ikechukwu AV, Murali S, Deepu R, Shivamurthy R (2021) Resnet-50 vs vgg-19 vs training from scratch: A comparative analysis of the segmentation and classification of pneumonia from chest x-ray images. Global Trans Proc 2(2):375\u2013381","journal-title":"Global Trans Proc"},{"key":"21105_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104534","volume":"224","author":"S Showkat","year":"2022","unstructured":"Showkat S, Qureshi S (2022) Efficacy of transfer learning-based resnet models in chest x-ray image classification for detecting covid-19 pneumonia. Chem Intell Lab Syst 224:104534","journal-title":"Chem Intell Lab Syst"},{"issue":"24","key":"21105_CR14","doi-asserted-by":"publisher","first-page":"13111","DOI":"10.3390\/app132413111","volume":"13","author":"SA Hasanah","year":"2023","unstructured":"Hasanah SA, Pravitasari AA, Abdullah AS, Yulita IN, Asnawi MH (2023) A deep learning review of resnet architecture for lung disease identification in cxr image. Appl Sci 13(24):13111","journal-title":"Appl Sci"},{"key":"21105_CR15","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.procs.2023.01.018","volume":"218","author":"S Sharma","year":"2023","unstructured":"Sharma S, Guleria K (2023) A deep learning based model for the detection of pneumonia from chest x-ray images using vgg-16 and neural networks. Procedia Comput Sci 218:357\u2013366","journal-title":"Procedia Comput Sci"},{"issue":"5","key":"21105_CR16","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1007\/s10489-020-02055-x","volume":"51","author":"C Sitaula","year":"2021","unstructured":"Sitaula C, Hossain MB (2021) Attention-based vgg-16 model for covid-19 chest x-ray image classification. Appl Intell 51(5):2850\u20132863","journal-title":"Appl Intell"},{"issue":"1","key":"21105_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1002\/ima.22812","volume":"33","author":"HA Sanghvi","year":"2023","unstructured":"Sanghvi HA, Patel RH, Agarwal A, Gupta S, Sawhney V, Pandya AS (2023) A deep learning approach for classification of covid and pneumonia using densenet-201. Int J Imaging Syst Technol 33(1):18\u201338","journal-title":"Int J Imaging Syst Technol"},{"issue":"2","key":"21105_CR18","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, Dama\u0161evi\u010dius R, De Albuquerque VHC (2020) A novel transfer learning based approach for pneumonia detection in chest x-ray images. Appl Sci 10(2):559","journal-title":"Appl Sci"},{"issue":"1","key":"21105_CR19","doi-asserted-by":"publisher","first-page":"767","DOI":"10.30574\/ijsra.2024.12.1.0880","volume":"2","author":"M Maniruzzaman","year":"2024","unstructured":"Maniruzzaman M, Sami A, Hoque R, Mandal P et al (2024) Pneumonia prediction using deep learning in chest x-ray images. Int J Sci Res Arch 2(1):767\u2013773","journal-title":"Int J Sci Res Arch"},{"issue":"21","key":"21105_CR20","doi-asserted-by":"publisher","first-page":"60789","DOI":"10.1007\/s11042-023-17965-4","volume":"83","author":"NW Asnake","year":"2024","unstructured":"Asnake NW, Salau AO, Ayalew AM (2024) X-ray image-based pneumonia detection and classification using deep learning. Multimed Tools Appl 83(21):60789\u201360807","journal-title":"Multimed Tools Appl"},{"key":"21105_CR21","doi-asserted-by":"crossref","unstructured":"V. Parthasarathy and S. Saravanan, Chaotic sea horse optimization with deep learning model for lung disease pneumonia detection and classification on chest x-ray images. Multimedia Tools and Applications, pp 1\u201323, 2024","DOI":"10.1007\/s11042-024-18301-0"},{"key":"21105_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105857","volume":"90","author":"AM Rifai","year":"2024","unstructured":"Rifai AM, Raharjo S, Utami E, Ariatmanto D (2024) Analysis for diagnosis of pneumonia symptoms using chest x-ray based on mobilenetv2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (clahe). Biomedical Signal Processing and Control 90:105857","journal-title":"Biomedical Signal Processing and Control"},{"issue":"2","key":"21105_CR23","first-page":"651","volume":"2","author":"D Kermany","year":"2018","unstructured":"Kermany D, Zhang K, Goldbaum M et al (2018) Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data 2(2):651","journal-title":"Mendeley data"},{"key":"21105_CR24","unstructured":"Rodrigo BM (2023) BFracture multi-region x-ray data. Accessed: 23 Jan 2025"},{"key":"21105_CR25","unstructured":"ari D (2023) Shoulder x-ray classification dataset. Accessed: 23 Jan 2025"},{"issue":"1","key":"21105_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12880-024-01546-4","volume":"5","author":"A Alam","year":"2025","unstructured":"Alam A, Al-Shamayleh AS, Thalji N, Raza A, Morales Barajas EA, Thompson EB, de la Torre Diez I, Ashraf I (2025) Novel transfer learning based bone fracture detection using radiographic images. BMC Medical Imaging 5(1):5","journal-title":"BMC Medical Imaging"},{"key":"21105_CR27","doi-asserted-by":"crossref","unstructured":"H. Mewada, J. F. Al-Asad, H. Patel, N. Mohammed (2024) Leveraging spatial and temporal features using cnn-lstm for improved bone fracture classification from x-ray images. In: 2024 6th International symposium on advanced electrical and communication technologies (ISAECT), pp 1\u20135, IEEE","DOI":"10.1109\/ISAECT64333.2024.10799900"},{"key":"21105_CR28","doi-asserted-by":"crossref","unstructured":"B. S. Pundir, R. Kumar, M. Gupta, A. J. Obaid (2024) A hybrid deep learning approach for bone defect detection using dcgan and cnn. In: 2024 First international conference on technological innovations and advance computing (TIACOMP), pp 379\u2013383, IEEE","DOI":"10.1109\/TIACOMP64125.2024.00070"},{"key":"21105_CR29","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.csbj.2020.04.005","volume":"18","author":"G Urban","year":"2020","unstructured":"Urban G, Porhemmat S, Stark M, Feeley B, Okada K, Baldi P (2020) Classifying shoulder implants in x-ray images using deep learning. Comput Struct Biotechnol J 18:967\u2013972","journal-title":"Comput Struct Biotechnol J"},{"issue":"6","key":"21105_CR30","doi-asserted-by":"publisher","first-page":"2723","DOI":"10.3390\/app11062723","volume":"11","author":"F Uysal","year":"2021","unstructured":"Uysal F, Hardala\u00e7 F, Peker O, Tolunay T, Tokg\u00f6z N (2021) Classification of shoulder x-ray images with deep learning ensemble models. Appl Sci 11(6):2723","journal-title":"Appl Sci"},{"key":"21105_CR31","doi-asserted-by":"crossref","unstructured":"Liu Y, Cheng D, Zhang D, Xu S, Han J (2024) Capsule networks with residual pose routing. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2023.3347722"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-21105-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-21105-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-21105-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T15:23:05Z","timestamp":1766676185000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-21105-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":31,"journal-issue":{"issue":"41","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21105"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-21105-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"2 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}