{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:27:41Z","timestamp":1778603261428,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"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":["J Big Data"],"DOI":"10.1186\/s40537-024-01018-0","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:34Z","timestamp":1729782214000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images"],"prefix":"10.1186","volume":"11","author":[{"given":"Tijana","family":"Geroski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ognjen","family":"Pavi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lazar","family":"Da\u0161i\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dragan","family":"Milovanovi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina","family":"Petrovi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nenad","family":"Filipovi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"1018_CR1","unstructured":"National Center for Health Statistics (NCHS). FastStats: Pneumonia. (Centers for Disease COntrol and Prevention (CDC)) Retrieved September 28, 2023. https:\/\/www.cdc.gov\/nchs\/fastats\/pneumonia.html"},{"key":"1018_CR2","unstructured":"World Health Organization. WHO Coronavirus (COVID-19) Dashboard. (World Health Organization) Retrieved September 28, 2023. https:\/\/covid19.who.int"},{"key":"1018_CR3","unstructured":"American Cancer Society. (2023). Facts & Figures 2023. (American Cancer Society) Retrieved September 28, 2023. https:\/\/www.cancer.org\/cancer\/types\/lung-cancer\/about\/key-statistics.html"},{"key":"1018_CR4","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.neucom.2021.08.159","volume":"489","author":"X Yu","year":"2022","unstructured":"Yu X, Wang J, Hong Q, Teku R, Wang SH, Zhang YD. Transfer learning for medical images analyses: a survey. Neurocomputing. 2022;489:230\u201354.","journal-title":"Neurocomputing"},{"key":"1018_CR5","first-page":"100003","volume":"2","author":"R Mehrotra","year":"2020","unstructured":"Mehrotra R, Ansari M, Agrawal R, Anand R. A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl. 2020;2:100003.","journal-title":"Mach Learn Appl"},{"key":"1018_CR6","volume-title":"Handbook of research on machine learning applications and trends: algorithms, methods, and techniques","author":"LA Torrey","year":"2010","unstructured":"Torrey LA. Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. Hershey: IGI Global; 2010."},{"issue":"11","key":"1018_CR7","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"key":"1018_CR8","unstructured":"Krizhevsky, A., Sutskever, I., & E, H. G. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012; 25."},{"key":"1018_CR9","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint. 2014.\u00a0arXiv:1409.1556."},{"key":"1018_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1018_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., & Anguelov, D. E. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1018_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1018_CR13","volume-title":"ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"X Wang","year":"2017","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Honolulu: IEEE; 2017."},{"issue":"11","key":"1018_CR14","doi-asserted-by":"publisher","first-page":"e100268","DOI":"10.1371\/journal.pmed.1002686","volume":"15","author":"P Rajpurkar","year":"2018","unstructured":"Rajpurkar P, Irvin J, Ball R, Zhu K, Yang B, Mehta H, Lungren M. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e100268.","journal-title":"PLoS Med"},{"key":"1018_CR15","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.cmpb.2018.04.025","volume":"161","author":"M Wo\u017aniak","year":"2018","unstructured":"Wo\u017aniak M, Po\u0142ap D, Capizzi G, Sciuto GL, Ko\u015bmider L, Frankiewicz K. Small lung nodules detection based on local variance analysis and probabilistic neural network. Comput Methods Programs Biomed. 2018;161:173\u201380.","journal-title":"Comput Methods Programs Biomed"},{"key":"1018_CR16","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"},{"key":"1018_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/s19173722","author":"N Nasrullah","year":"2019","unstructured":"Nasrullah N, Jun S, Mohammad SA, Muhammad M, Bin C, Haibo H. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors. 2019. https:\/\/doi.org\/10.3390\/s19173722.","journal-title":"Sensors"},{"key":"1018_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002697","author":"GT Andrew","year":"2018","unstructured":"Andrew GT, Clinton M, John M. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: a retrospective study. PLoS Med. 2018. https:\/\/doi.org\/10.1371\/journal.pmed.1002697.","journal-title":"PLoS Med"},{"key":"1018_CR19","unstructured":"ChestX-ray14. (n.d.). Retrieved May 22, 2023. https:\/\/www.v7labs.com\/open-datasets\/chestx-ray14"},{"issue":"4","key":"1018_CR20","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1142\/S0218001409007326","volume":"23","author":"Y Sun","year":"2009","unstructured":"Sun Y, Wong AK, Kamel MS. Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell. 2009;23(4):687\u2013719.","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"1018_CR21","doi-asserted-by":"crossref","unstructured":"Somasundaram, A., & Reddy, U. S. Data Imbalance: Effects and Solutions for Classification of Large and Highly Imbalanced Data. 1st International Conference on Research in Engineering, Computers and Technology(ICRECT). Tiruchirappalli. 2016.","DOI":"10.1109\/ICCIDS.2017.8272643"},{"key":"1018_CR22","unstructured":"Association, A. L. Lung health diseases. https:\/\/www.lung.org\/lung-health-diseases\/lung-procedures-and-tests\/ct-scan. 2024."},{"issue":"19","key":"1018_CR23","doi-asserted-by":"publisher","first-page":"4130","DOI":"10.3390\/app9194130","volume":"9","author":"T Khanh Ho","year":"2019","unstructured":"Khanh Ho T, Gwak J. Multiple feature integration for classification of thoracic disease in chest radiography. Appl Sci. 2019;9(19):4130.","journal-title":"Appl Sci"},{"issue":"1","key":"1018_CR24","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1148\/radiol.2018180237","volume":"290","author":"J Nam","year":"2019","unstructured":"Nam J, Park S, Hwang E, Lee J, Jin K, Lim K. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 2019;290(1):218\u201328.","journal-title":"Radiology"},{"key":"1018_CR25","doi-asserted-by":"publisher","DOI":"10.1763\/rscbjbr9sj.2","author":"D Kermany","year":"2018","unstructured":"Kermany D, Zhang K, Goldbaum M. Labeled optical coherence tomography (OCT) and chest X-Ray images for classification. Mendeley Data v2. 2018. https:\/\/doi.org\/10.1763\/rscbjbr9sj.2.","journal-title":"Mendeley Data v2"},{"key":"1018_CR26","unstructured":"Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (n.d.). COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv preprint, arXiv:2006.11988. Retrieved from https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"1018_CR27","doi-asserted-by":"publisher","unstructured":"Tahir, A. M., Chowdhury, M. E., Qiblaway, Y., Khandakar, A., Rahmam, T., Kiranyaz, S., Ezeddin, M. (2021). https:\/\/doi.org\/10.34740\/kaggle\/dsv\/3122958","DOI":"10.34740\/kaggle\/dsv\/3122958"},{"key":"1018_CR28","unstructured":"Dung, N. B., Nguyen, H. Q., Elliott, J., & et al. Retrieved 02 24, 2024, from VinBigData Chest X-ray Abnormalities Detection: https:\/\/www.kaggle.com\/c\/vinbigdata-chest-xray-abnormalities-detection. 2020."},{"key":"1018_CR29","doi-asserted-by":"publisher","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","volume":"8","author":"T Rahman","year":"2020","unstructured":"Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Ayari MA. Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access. 2020;8:191586\u2013601.","journal-title":"IEEE Access"},{"key":"1018_CR30","unstructured":"Zawacki, A., Wu, C., Shih, G., Elliot, J., Fomitchev, M., Hussain, M., Bao, S. (2019). Retrieved 02 24, 2024, from SIIM-ACR Pneumothorax Segmentation: https:\/\/www.kaggle.com\/c\/siim-acr-pneumothorax-segmentation"},{"key":"1018_CR31","doi-asserted-by":"publisher","DOI":"10.1763\/9xkhgts2s6.1","author":"U Sait","year":"2020","unstructured":"Sait U, Lal KG, Prajapati S, Bhaumik R, Kumar T, Sanjana S, Bhalla K. Curated dataset for COVID-19 posterior-anterior chest radiography images (X-Rays). Mendeley Data. 2020. https:\/\/doi.org\/10.1763\/9xkhgts2s6.1.","journal-title":"Mendeley Data"},{"issue":"6","key":"1018_CR32","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/S2213-2600(20)30105-3","volume":"8","author":"J Soriano","year":"2020","unstructured":"Soriano J, Kendrick P, Paulson K, Gupta V, Abrams E, Adedoyin RE. Prevalence and attributable health burden of chronic respiratory diseases, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. Lancet Respir Med. 2020;8(6):585\u201396.","journal-title":"Lancet Respir Med"},{"key":"1018_CR33","unstructured":"Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint. 2017. arXiv:1710.10501."},{"key":"1018_CR34","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/978-3-030-13469-3_88","volume-title":"Progress in pattern recognition, image analysis, computer vision and applications: 23rd iberoamerican congress, CIARP 2018","author":"S Guendel","year":"2019","unstructured":"Guendel S, Grbic S, Georgescu B, Liu S, Maier A, Comaniciu D. Learning to recognize abnormalities in chest x-rays with location-aware dense networks. In: Vera-Rodriguez R, Fierrez J, Morales A, editors. Progress in pattern recognition, image analysis, computer vision and applications: 23rd iberoamerican congress, CIARP 2018. Madrid: Springer International Publishing; 2019. p. 757\u201365."},{"issue":"9","key":"1018_CR35","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1136\/oemed-2019-106386","volume":"77","author":"X Wang","year":"2020","unstructured":"Wang X, Yu J, Zhu Q, Li S, Zhao Z, Yang B, Pu J. Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography. Occup Environ Med. 2020;77(9):597\u2013602.","journal-title":"Occup Environ Med"},{"issue":"2","key":"1018_CR36","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/app10020559","volume":"10","author":"V Chouhan","year":"2020","unstructured":"Chouhan V, Singh S, Khamparia A, Gupta D, Tiwari P, Moreira C, De Albuquerque V. A novel transfer learning based approach for pneumonia detection in chest X-ray Images. Appl Sci. 2020;10(2):559.","journal-title":"Appl Sci"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01018-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-024-01018-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01018-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:59Z","timestamp":1729782239000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-024-01018-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,24]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1018"],"URL":"https:\/\/doi.org\/10.1186\/s40537-024-01018-0","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,24]]},"assertion":[{"value":"14 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2024","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 Ethical Approval was obtained from the Ethical Committee of the University Clinical Center Kragujevac under the number 01\/23\/111 on 10.04.2023.","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 no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"146"}}