{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T23:59:20Z","timestamp":1792627160865,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-29633-8","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T10:38:03Z","timestamp":1763980683000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Advanced COVID-19 detection using cough signals with space reconstruction and 3D deep convolutional neural networks"],"prefix":"10.1038","volume":"15","author":[{"given":"Aymen A.","family":"Altae","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdolvahab","family":"Ehsani Rad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyvan","family":"Mohebbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"29633_CR1","unstructured":"WHO. Number of COVID-19 Cases Reported To WHO (World Health Organization, 2024)."},{"issue":"12","key":"29633_CR2","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1164\/rccm.202004-1163LE","volume":"201","author":"DR Ziehr","year":"2020","unstructured":"Ziehr, D. R. et al. Corey Hardin. Respiratory pathophysiology of mechanically ventilated patients with COVID-19: a cohort study. Am. J. Respir. Crit Care Med. 201 (12), 1560\u20131564 (2020).","journal-title":"Am. J. Respir. Crit Care Med."},{"issue":"1","key":"29633_CR3","doi-asserted-by":"publisher","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., Lin, Z. Q. & Wong, A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10 (1), 19549 (2020).","journal-title":"Sci. Rep."},{"issue":"2","key":"29633_CR4","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1016\/j.aej.2021.06.024","volume":"61","author":"K Lella","year":"2022","unstructured":"Lella, K., Kumar & Alphonse Pja. Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath. Alexandria Eng. J. 61 (2), 1319\u20131334 (2022).","journal-title":"Alexandria Eng. J."},{"key":"29633_CR5","doi-asserted-by":"crossref","unstructured":"Marini, J. J. & Luciano Gattinoni. and. Management of COVID-19 respiratory distress. Jama 323(22), 2329\u20132330. (2020).","DOI":"10.1001\/jama.2020.6825"},{"key":"29633_CR6","doi-asserted-by":"publisher","first-page":"106181","DOI":"10.1016\/j.compbiomed.2022.106181","volume":"150","author":"G Wang","year":"2022","unstructured":"Wang, G., Guo, S., Han, L., Song, X. & Zhao, Y. Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on Whale optimized support vector machine and improved DS evidence fusion. Comput. Biol. Med. 150, 106181 (2022).","journal-title":"Comput. Biol. Med."},{"key":"29633_CR7","doi-asserted-by":"crossref","unstructured":"Chen, W., S\u00e1, R. C., Bai, Y., Napel, S. & Gevaert, O. Diane S. Lauderdale, and Maryellen L. Giger. Machine learning with multimodal data for COVID-19. Heliyon. 9(7). (2023).","DOI":"10.1016\/j.heliyon.2023.e17934"},{"key":"29633_CR8","first-page":"200160","volume":"17","author":"N Nasir","year":"2023","unstructured":"Nasir, N., Kansal, A., Barneih, F., Al-Shaltone, O. & Bonny, T. Mohammad al-Shabi, and Ahmed al Shammaa. Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. Intell. Syst. Appl. 17, 200160 (2023).","journal-title":"Intell. Syst. Appl."},{"key":"29633_CR9","doi-asserted-by":"publisher","first-page":"104572","DOI":"10.1016\/j.compbiomed.2021.104572","volume":"135","author":"M Pahar","year":"2021","unstructured":"Pahar, M., Klopper, M., Warren, R. & Niesler, T. COVID-19 cough classification using machine learning and global smartphone recordings. Comput. Biol. Med. 135, 104572 (2021).","journal-title":"Comput. Biol. Med."},{"key":"29633_CR10","doi-asserted-by":"publisher","first-page":"105153","DOI":"10.1016\/j.compbiomed.2021.105153","volume":"141","author":"M Pahar","year":"2022","unstructured":"Pahar, M., Klopper, M., Warren, R. & Niesler, T. COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features. Comput. Biol. Med. 141, 105153 (2022).","journal-title":"Comput. Biol. Med."},{"key":"29633_CR11","doi-asserted-by":"publisher","first-page":"104399","DOI":"10.1016\/j.compbiomed.2021.104399","volume":"133","author":"H Quan","year":"2021","unstructured":"Quan, H., Xu, X., Zheng, T., Li, Z. & Zhao, M. DenseCapsNet: detection of COVID-19 from X-ray images using a capsule neural network. Comput. Biol. Med. 133, 104399 (2021).","journal-title":"Comput. Biol. Med."},{"key":"29633_CR12","doi-asserted-by":"publisher","first-page":"112137","DOI":"10.1016\/j.asoc.2024.112137","volume":"166","author":"S Maheswari","year":"2024","unstructured":"Maheswari, S., Suresh, S. & Ahamed Ali, S. A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray images. Appl. Soft Comput. 166, 112137 (2024).","journal-title":"Appl. Soft Comput."},{"key":"29633_CR13","doi-asserted-by":"publisher","first-page":"104944","DOI":"10.1016\/j.compbiomed.2021.104944","volume":"138","author":"V Despotovic","year":"2021","unstructured":"Despotovic, V., Ismael, M., Cornil, M., Call, R. M. & Fagherazzi, G. Detection of COVID-19 from voice, cough and breathing patterns: dataset and preliminary results. Comput. Biol. Med. 138, 104944 (2021).","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"29633_CR14","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/j.jvoice.2021.05.015","volume":"37","author":"O Zealouk","year":"2023","unstructured":"Zealouk, O. et al. Analysis of COVID-19 resulting cough using formants and automatic speech recognition system. J. Voice. 37 (6), 971\u2013e9 (2023).","journal-title":"J. Voice"},{"key":"29633_CR15","doi-asserted-by":"publisher","first-page":"103703","DOI":"10.1016\/j.bspc.2022.103703","volume":"76","author":"G Sharma","year":"2022","unstructured":"Sharma, G., Umapathy, K. & Krishnan, S. Audio texture analysis of COVID-19 cough, breath, and speech sounds. Biomed. Signal Process. Control. 76, 103703 (2022).","journal-title":"Biomed. Signal Process. Control"},{"issue":"9","key":"29633_CR16","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s42452-024-06156-x","volume":"6","author":"A Abdel-Ghani","year":"2024","unstructured":"Abdel-Ghani, A. et al. Wearable bracelet and machine learning for remote diagnosis and pandemic infection detection. Discover Appl. Sci. 6 (9), 451 (2024).","journal-title":"Discover Appl. Sci."},{"issue":"1","key":"29633_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s13755-024-00283-w","volume":"12","author":"K Lella","year":"2024","unstructured":"Lella, K., Kumar, M. S., Jagadeesh & Alphonse, P. J. A. Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN. Health Inform. Sci. Syst. 12 (1), 22 (2024).","journal-title":"Health Inform. Sci. Syst."},{"issue":"1","key":"29633_CR18","first-page":"5","volume":"16","author":"B Atmaja","year":"2024","unstructured":"Atmaja, B. & Tris Zanjabila, Suyanto, and Akira Sasou. Comparing hysteresis comparator and RMS threshold methods for automatic single cough segmentations. Int. J. Inform. Technol. 16 (1), 5\u201312 (2024).","journal-title":"Int. J. Inform. Technol."},{"issue":"1","key":"29633_CR19","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1186\/s12911-024-02646-5","volume":"24","author":"M Mousavi","year":"2024","unstructured":"Mousavi, M. & Hosseini, S. A deep convolutional neural network approach using medical image classification. BMC Med. Inf. Decis. Mak. 24 (1), 239 (2024).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"29633_CR20","doi-asserted-by":"publisher","first-page":"106165","DOI":"10.1016\/j.bspc.2024.106165","volume":"93","author":"EK Topuz","year":"2024","unstructured":"Topuz, E. K. & Kaya, Y. SUPER-COUGH: A super learner-based ensemble machine learning method for detecting disease on cough acoustic signals. Biomed. Signal Process. Control. 93, 106165 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"29633_CR21","unstructured":"Haritaoglu, E. et al. Using deep learning with large aggregated datasets for COVID-19 classification from cough. ArXiv Preprint ArXiv:2201.01669 ( (2022)."},{"key":"29633_CR22","doi-asserted-by":"crossref","unstructured":"Sangle, S. and Chandrakant Gaikwad. Covid-19 detection using spectral and statistical features of cough and breath sounds. In 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 182\u2013186. IEEE, (2021).","DOI":"10.1109\/DASA53625.2021.9682308"},{"issue":"1","key":"29633_CR23","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s00034-023-02474-4","volume":"43","author":"SB Sangle","year":"2024","unstructured":"Sangle, S. B., Chandrakant, J. & Gaikwad COVID-19 respiratory sound signal detection using HOS-based linear frequency cepstral coefficients and deep learning. Circuits Syst. Signal. Process. 43 (1), 331\u2013347 (2024).","journal-title":"Circuits Syst. Signal. Process."},{"issue":"7","key":"29633_CR24","doi-asserted-by":"publisher","first-page":"3629","DOI":"10.1007\/s11760-023-02589-w","volume":"17","author":"SB Sangle","year":"2023","unstructured":"Sangle, S. B. & Chandrakant, J. Gaikwad. Accumulated bispectral image-based respiratory sound signal classification using deep learning. Signal. Image Video Process. 17 (7), 3629\u20133636 (2023).","journal-title":"Image Video Process."},{"key":"29633_CR25","doi-asserted-by":"publisher","first-page":"108368","DOI":"10.1016\/j.compbiolchem.2025.108368","volume":"115","author":"DP Singh","year":"2025","unstructured":"Singh, D. P., Banerjee, T., Kour, P., Swain, D. & Narayan, Y. CICADA (UCX): A novel approach for automated breast cancer classification through aggressiveness delineation. Comput. Biol. Chem. 115, 108368 (2025).","journal-title":"Comput. Biol. Chem."},{"key":"29633_CR26","doi-asserted-by":"crossref","unstructured":"Singh, D. P., Kour, P., Banerjee, T. & Swain, D. A comprehensive review of various machine learning and deep learning models for anti-cancer drug response prediction: comparative analysis with existing state of the Art methods. Archives Comput. Methods Engineering. 1\u201325. (2025).","DOI":"10.1007\/s11831-025-10255-2"},{"key":"29633_CR27","doi-asserted-by":"crossref","unstructured":"Banerjee, T. Towards automated and reliable lung cancer detection in histopathological images using DY-FSPAN: A feature-summarized pyramidal attention network for explainable AI. Computational Biology Chemistry. 108500. (2025).","DOI":"10.1016\/j.compbiolchem.2025.108500"},{"issue":"1","key":"29633_CR28","doi-asserted-by":"publisher","first-page":"27207","DOI":"10.1038\/s41598-025-12602-6","volume":"15","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. et al. A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation. Sci. Rep. 15 (1), 27207 (2025).","journal-title":"Sci. Rep."},{"issue":"5","key":"29633_CR29","doi-asserted-by":"publisher","first-page":"e70034","DOI":"10.1002\/jdn.70034","volume":"85","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. Electromagnetic interaction algorithm (EIA)-Based feature selection with adaptive kernel attention network (AKAttNet) for autism spectrum disorder classification. Int. J. Dev. Neurosci. 85 (5), e70034 (2025).","journal-title":"Int. J. Dev. Neurosci."},{"issue":"1","key":"29633_CR30","doi-asserted-by":"publisher","first-page":"28669","DOI":"10.1038\/s41598-025-11574-x","volume":"15","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. et al. Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches. Sci. Rep. 15 (1), 28669 (2025).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29633-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29633-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29633-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:02:42Z","timestamp":1777402962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29633-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["29633"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-29633-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"20 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"45275"}}