{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:44:55Z","timestamp":1791391495813,"version":"4.3.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"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":["Sci Rep"],"DOI":"10.1038\/s41598-025-20635-0","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:33:43Z","timestamp":1761125623000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Image complexity-based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning"],"prefix":"10.1038","volume":"15","author":[{"given":"Debanjali","family":"Bhattacharya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neelam","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Yashwanth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Chattopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"20635_CR1","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3389\/fnins.2019.00585","volume":"13","author":"A Farahani","year":"2019","unstructured":"Farahani, A., Farzad, V. W. K. & Lighthall, N. R. Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Front. Neurosci. 13, 585. https:\/\/doi.org\/10.3389\/fnins.2019.00585 (2019).","journal-title":"Front. Neurosci."},{"key":"20635_CR2","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/hbm.460020107","volume":"2","author":"KJ Friston","year":"1994","unstructured":"Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56\u201378. https:\/\/doi.org\/10.1002\/hbm.460020107 (1994).","journal-title":"Hum. Brain Mapp."},{"key":"20635_CR3","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s12264-013-1421-0","volume":"30","author":"Z Dai","year":"2014","unstructured":"Dai, Z. & He, Y. Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer\u2019s disease. Neurosci. Bull. 30, 217\u2013232. https:\/\/doi.org\/10.1007\/s12264-013-1421-0 (2014).","journal-title":"Neurosci. Bull."},{"key":"20635_CR4","doi-asserted-by":"publisher","first-page":"45347","DOI":"10.1038\/srep45347","volume":"7","author":"H Abos","year":"2017","unstructured":"Abos, H., Baggio, A. & Segura, B. Discriminating cognitive status in Parkinson\u2019s disease through functional connectomics and machine learning. Sci. Rep. 7, 45347. https:\/\/doi.org\/10.1038\/srep45347 (2017).","journal-title":"Sci. Rep."},{"key":"20635_CR5","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neuroscience.2017.10.033","volume":"403","author":"A Fleischer","year":"2017","unstructured":"Fleischer, A., Radetz, V. & Ciolac, D. Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts. Neuroscience 403, 35\u201353. https:\/\/doi.org\/10.1016\/j.neuroscience.2017.10.033 (2017).","journal-title":"Neuroscience"},{"key":"20635_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jneumeth.2017.03.006","volume":"282","author":"A Hojjati","year":"2017","unstructured":"Hojjati, A., Ebrahimzadeh, S. H. & Khazaee, A. Predicting conversion from mci to ad using resting-state fmri, graph theoretical approach and svm. J. Neurosci. Methods 282, 69\u201380. https:\/\/doi.org\/10.1016\/j.jneumeth.2017.03.006 (2017).","journal-title":"J. Neurosci. Methods"},{"key":"20635_CR7","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.bspc.2017.10.006","volume":"40","author":"D Miri Ashtiani","year":"2018","unstructured":"Miri Ashtiani, D. et al. Altered topological properties of brain networks in the early ms patients revealed by cognitive taskrelated fmri and graph theory. Biomed. Signal Process. Control 40, 385\u2013395. https:\/\/doi.org\/10.1016\/j.bspc.2017.10.006 (2018).","journal-title":"Biomed. Signal Process. Control"},{"key":"20635_CR8","doi-asserted-by":"publisher","unstructured":"Murthy, O.\u00a0E. A machine learning approach for diagnosing neurological disorders using longitudinal resting-state fmri. In International Conference on Cloud Computing, Data Science and Engineering (Confluence) 494\u2013499. https:\/\/doi.org\/10.1109\/Confluence51648.2021.9377173 (2021).","DOI":"10.1109\/Confluence51648.2021.9377173"},{"key":"20635_CR9","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1177\/09544119221134991","volume":"236","author":"R Devisetty","year":"2022","unstructured":"Devisetty, R. & Amsitha, M. Localizing epileptogenic network from seeg using non-linear correlation, mutual information and graph theory analysis. Proc. of the Institution of Mechanical Engineers. J. Eng. Med. 236, 1783\u20131796 (2022).","journal-title":"J. Eng. Med."},{"key":"20635_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-025-03389-9","author":"D Bhattacharya","year":"2025","unstructured":"Bhattacharya, D. & Sinha, N. Towards understanding the functional connectivity patterns in visual brain network. Med. Biol. Eng. Comput. https:\/\/doi.org\/10.1007\/s11517-025-03389-9 (2025).","journal-title":"Med. Biol. Eng. Comput."},{"key":"20635_CR11","doi-asserted-by":"publisher","first-page":"1518984","DOI":"10.3389\/fnins.2025.1518984","volume":"19","author":"D Bhattacharya","year":"2025","unstructured":"Bhattacharya, D., Kaur, R., Aithal, N., Sinha, N. & Gregor Issac, T. Persistent homology for mci classification: a comparative analysis between graph and vietoris-rips filtrations. Front. Neurosci. 19, 1518984. https:\/\/doi.org\/10.3389\/fnins.2025.1518984 (2025).","journal-title":"Front. Neurosci."},{"key":"20635_CR12","doi-asserted-by":"crossref","unstructured":"Ktena, S. I. et al. Distance metric learning using graph convolutional networks: Application to functional brain networks. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017 (eds Descoteaux, M. et al.) 469\u2013477 (Springer, 2017).","DOI":"10.1007\/978-3-319-66182-7_54"},{"key":"20635_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2019.05.043","volume":"499","author":"Z Mao","year":"2019","unstructured":"Mao, Z. et al. Spatio-temporal deep learning method for adhd fmri classification. Inf. Sci. 499, 1\u201311. https:\/\/doi.org\/10.1016\/j.ins.2019.05.043 (2019).","journal-title":"Inf. Sci."},{"key":"20635_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/s41597-019-0052-3","volume":"6","author":"J Chang","year":"2019","unstructured":"Chang, J. et al. Bold5000, a public fmri dataset while viewing 5000 visual images. Sci. Data 6, 49. https:\/\/doi.org\/10.1038\/s41597-019-0052-3 (2019).","journal-title":"Sci. Data"},{"key":"20635_CR15","doi-asserted-by":"publisher","unstructured":"Kancharala, V.\u00a0K., Bhattacharya, D. & Sinha, N. Spatial encoding of bold fmri time series for categorizing static images across visual datasets: A pilot study on human vision. In TENCON 2023\u20142023 IEEE Region 10 Conference (TENCON) 1117\u20131122. https:\/\/doi.org\/10.1109\/TENCON58879.2023.10322476 (2023).","DOI":"10.1109\/TENCON58879.2023.10322476"},{"key":"20635_CR16","doi-asserted-by":"publisher","unstructured":"Kanigiri, N., Suggula, M., Bhattacharya, D. & Sinha, N. Investigating the changes in bold responses during viewing of images with varied complexity: An fmri time-series based analysis on human vision. In Proceedings of the Third International Conference on AI-ML Systems, AIMLSystems \u201923. https:\/\/doi.org\/10.1145\/3639856.3639862 (Association for Computing Machinery, 2024).","DOI":"10.1145\/3639856.3639862"},{"key":"20635_CR17","doi-asserted-by":"publisher","unstructured":"Lee, H., Chung, M.\u00a0K., Kang, H., Kim, B.-N. & Lee, D.\u00a0S. Discriminative persistent homology of brain networks. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 841\u2013844. https:\/\/doi.org\/10.1109\/ISBI.2011.5872535 (2011).","DOI":"10.1109\/ISBI.2011.5872535"},{"key":"20635_CR18","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1109\/TMI.2012.2219590","volume":"31","author":"H Lee","year":"2012","unstructured":"Lee, H., Kang, H., Chung, M., Kim, B. & Lee, D. Persistent brain network homology from the perspective of dendrogram. IEEE Trans. Med. Imaging 31, 590. https:\/\/doi.org\/10.1109\/TMI.2012.2219590 (2012).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20635_CR19","doi-asserted-by":"crossref","unstructured":"Aithal, N., Bhattacharya, D., Sinha, N. & Issac, T.\u00a0G. Leveraging persistent homology for differential diagnosis of mild cognitive impairment. In Pattern Recognition (eds Antonacopoulos, A. et al.), 17\u201332 (Springer, 2025).","DOI":"10.1007\/978-3-031-78198-8_2"},{"key":"20635_CR20","doi-asserted-by":"crossref","unstructured":"Bhattacharya, D., Aithal, N., Jayswal, M. & Sinha, N. Analyzing brain tumor connectomics using graphs and persistent homology. In Topology- and Graph-Informed Imaging Informatics (eds Chen, C. et al.), 33\u201342 (Springer, 2025).","DOI":"10.1007\/978-3-031-73967-5_4"},{"key":"20635_CR21","doi-asserted-by":"publisher","unstructured":"Merelli, E., Piangerelli, M., Rucco, M. & Toller, D. A topological approach for multivariate time series characterization: The epileptic brain. In Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONETICS), BICT\u201915 201\u2013204, https:\/\/doi.org\/10.4108\/eai.3-12-2015.2262525 (ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2016).","DOI":"10.4108\/eai.3-12-2015.2262525"},{"key":"20635_CR22","doi-asserted-by":"publisher","first-page":"035006","DOI":"10.1088\/2632-072X\/abb4c6","volume":"2","author":"BJ Stolz","year":"2021","unstructured":"Stolz, B. J., Emerson, T., Nahkuri, S., Porter, M. A. & Harrington, H. A. Topological data analysis of task-based fmri data from experiments on schizophrenia. J. Phys. Complex. 2, 035006. https:\/\/doi.org\/10.1088\/2632-072X\/abb4c6 (2021).","journal-title":"J. Phys. Complex."},{"key":"20635_CR23","doi-asserted-by":"publisher","first-page":"118245","DOI":"10.1016\/j.neuroimage.2021.118245","volume":"238","author":"L Caputi","year":"2021","unstructured":"Caputi, L., Pidnebesna, A. & Hlinka, J. Promises and pitfalls of topological data analysis for brain connectivity analysis. Neuroimage 238, 118245. https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118245 (2021).","journal-title":"Neuroimage"},{"key":"20635_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0276419","volume":"18","author":"S Das","year":"2023","unstructured":"Das, S., Anand, D. V. & Chung, M. K. Topological data analysis of human brain networks through order statistics. PLoS ONE 18, 1\u201326. https:\/\/doi.org\/10.1371\/journal.pone.0276419 (2023).","journal-title":"PLoS ONE"},{"key":"20635_CR25","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1146\/annurev-vision-091718-014809","volume":"5","author":"A Russell","year":"2019","unstructured":"Russell, A. & Chris, I. B. Scene perception in the human brain. Annu. Rev. Vis. Sci. 5, 373\u2013397. https:\/\/doi.org\/10.1146\/annurev-vision-091718-014809 (2019).","journal-title":"Annu. Rev. Vis. Sci."},{"key":"20635_CR26","doi-asserted-by":"publisher","first-page":"9410","DOI":"10.1523\/JNEUROSCI.0852-19.2019","volume":"39","author":"AT Morgan","year":"2019","unstructured":"Morgan, A. T., Petro, L. S. & Muckli, L. Scene representations conveyed by cortical feedback to early visual cortex can be described by line drawings. J. Neurosci. 39, 9410\u20139423. https:\/\/doi.org\/10.1523\/JNEUROSCI.0852-19.2019 (2019).","journal-title":"J. Neurosci."},{"key":"20635_CR27","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1016\/j.neuron.2013.06.034","volume":"79","author":"ES Dustin","year":"2013","unstructured":"Dustin, E. S., Thomas, N. & Jack, L. G. Natural scene statistics account for the representation of scene categories in human visual cortex. Neuron 79, 1025\u20131034. https:\/\/doi.org\/10.1016\/j.neuron.2013.06.034 (2013).","journal-title":"Neuron"},{"key":"20635_CR28","doi-asserted-by":"crossref","unstructured":"Gauen, K. Comparison of visual datasets for machine learning. In Proc. IEEE International Conference on Information Reuse and Integration (IRI) 346\u2013355 (2017).","DOI":"10.1109\/IRI.2017.59"},{"key":"20635_CR29","unstructured":"Bilal, A. & Bhattacharya, P. A tour of visualization techniques for computer vision datasets. http:\/\/arxiv.org\/abs\/2204.08601v1 (2022)."},{"key":"20635_CR30","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3389\/fnins.2016.00123","volume":"10","author":"J Wang","year":"2016","unstructured":"Wang, J. et al. An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Front. Neurosci. 10, 123. https:\/\/doi.org\/10.3389\/fnins.2016.00123 (2016).","journal-title":"Front. Neurosci."},{"issue":"4","key":"20635_CR31","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1089\/brain.2014.0319","volume":"5","author":"W Junghi","year":"2015","unstructured":"Junghi, W. et al. Testing group differences in brain functional connectivity: Using correlations or partial correlations? Brain Connect. 5(4), 319. https:\/\/doi.org\/10.1089\/brain.2014.0319 (2015).","journal-title":"Brain Connect."},{"key":"20635_CR32","unstructured":"The GUDHI Project. GUDHI User and Reference Manual (GUDHI Editorial Board, 2020)."},{"key":"20635_CR33","unstructured":"John, A. et al. Spm Toolbox."},{"key":"20635_CR34","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 52, 1059\u20131069. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.10.003 (2010).","journal-title":"Neuroimage"},{"key":"20635_CR35","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1016\/j.neuroimage.2012.01.022","volume":"62","author":"SM Smith","year":"2012","unstructured":"Smith, S. M. The future of fmri connectivity. Neuroimage 62, 1257\u20131266. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.022 (2012).","journal-title":"Neuroimage"},{"key":"20635_CR36","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1016\/j.neuroimage.2010.08.063","volume":"54","author":"K Smith","year":"2011","unstructured":"Smith, K. et al. Network modelling methods for fmri. Neuroimage 54, 875\u2013891. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.08.063 (2011).","journal-title":"Neuroimage"},{"key":"20635_CR37","doi-asserted-by":"publisher","first-page":"100026","DOI":"10.1016\/j.acags.2020.100026","volume":"6","author":"E Ionas","year":"2020","unstructured":"Ionas, E. Partial correlations in compositional data analysis. Appl. Comput. Geosci. 6, 100026 (2020).","journal-title":"Appl. Comput. Geosci."},{"issue":"4","key":"20635_CR38","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1080\/19466315.2020.1784780","volume":"13","author":"E Shan","year":"2021","unstructured":"Shan, E. et al. Partial correlation coefficient for a study with repeated measurements. Stat. Biopharm. Res. 13(4), 448\u2013454. https:\/\/doi.org\/10.1080\/19466315.2020.1784780 (2021).","journal-title":"Stat. Biopharm. Res."},{"key":"20635_CR39","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.3390\/su13031033","volume":"13","author":"F Zhang","year":"2021","unstructured":"Zhang, F. et al. Partial correlation analysis of association between subjective well-being and ecological footprint. Sustainability 13, 1033. https:\/\/doi.org\/10.3390\/su13031033 (2021).","journal-title":"Sustainability"},{"key":"20635_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-020-0251-z","author":"T Millington","year":"2020","unstructured":"Millington, T. & Niranjan, M. Partial correlation financial networks. Appl. Netw. Sci. https:\/\/doi.org\/10.1007\/s41109-020-0251-z (2020).","journal-title":"Appl. Netw. Sci."},{"key":"20635_CR41","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1002\/hbm.1026","volume":"13","author":"K Saad","year":"2001","unstructured":"Saad, K. et al. Analysis and use of fmri response delays. Hum. Brain Mapp. 13, 74\u201393. https:\/\/doi.org\/10.1002\/hbm.1026 (2001).","journal-title":"Hum. Brain Mapp."},{"key":"20635_CR42","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/1471-2121-8-S1-S5","volume":"8","author":"A Cecchi","year":"2007","unstructured":"Cecchi, A. et al. Identifying directed links in large scale functional networks: application to brain fmri. BMC Cell Biol. 8, 74\u201393. https:\/\/doi.org\/10.1186\/1471-2121-8-S1-S5 (2007).","journal-title":"BMC Cell Biol."},{"key":"20635_CR43","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1016\/j.mri.2009.06.004","volume":"1","author":"F Giove","year":"2009","unstructured":"Giove, F. et al. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magn. Reson. Imaging 1, 1058\u20131064. https:\/\/doi.org\/10.1016\/j.mri.2009.06.004 (2009).","journal-title":"Magn. Reson. Imaging"},{"key":"20635_CR44","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1016\/j.neuroimage.2008.09.036","volume":"1","author":"K Murphy","year":"2009","unstructured":"Murphy, K. et al. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 1, 893\u2013905. https:\/\/doi.org\/10.1016\/j.neuroimage.2008.09.036 (2009).","journal-title":"Neuroimage"},{"key":"20635_CR45","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1016\/j.neuroimage.2009.05.005","volume":"1","author":"A Weissenbacher","year":"2009","unstructured":"Weissenbacher, A. et al. Correlations and anticorrelations in resting-state functional connectivity mri: a quantitative comparison of preprocessing strategies. Neuroimage 1, 1408\u20131416. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.05.005 (2009).","journal-title":"Neuroimage"},{"key":"20635_CR46","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s00454-002-2885-2","volume":"28","author":"H Edelsbrunner","year":"2002","unstructured":"Edelsbrunner, H., Letscher, D. & Zomorodian, A. Topological persistence and simplification. Discret. Comput. Geom. 28, 511\u2013533. https:\/\/doi.org\/10.1007\/s00454-002-2885-2 (2002).","journal-title":"Discret. Comput. Geom."},{"key":"20635_CR47","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s00454-004-1146-y","volume":"33","author":"A Zomorodian","year":"2005","unstructured":"Zomorodian, A. & Carlsson, G. Computing persistent homology. Discrete Comput. Geom. 33, 249\u2013274 (2005).","journal-title":"Discrete Comput. Geom."},{"key":"20635_CR48","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1090\/S0273-0979-09-01249-X","volume":"46","author":"G Carlsson","year":"2009","unstructured":"Carlsson, G. Topology and data. Bull. Am. Math. Soc. 46, 255\u2013308. https:\/\/doi.org\/10.1090\/S0273-0979-09-01249-X (2009).","journal-title":"Bull. Am. Math. Soc."},{"key":"20635_CR49","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00454-006-1276-5","volume":"37","author":"D Cohen-Steiner","year":"2007","unstructured":"Cohen-Steiner, D., Edelsbrunner, H. & Harer, J. Stability of persistence diagrams. Discret. Comput. Geom. 37, 103\u2013120 (2007).","journal-title":"Discret. Comput. Geom."},{"key":"20635_CR50","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patrec.2015.12.012","volume":"83","author":"O Dunaeva","year":"2016","unstructured":"Dunaeva, O. et al. The classification of endoscopy images with persistent homology. Pattern Recogn. Lett. 83, 13\u201322. https:\/\/doi.org\/10.1016\/j.patrec.2015.12.012 (2016).","journal-title":"Pattern Recogn. Lett."},{"key":"20635_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-23424-0","volume":"8","author":"J Brodzki","year":"2018","unstructured":"Brodzki, J. et al. Lung topology characteristics in patients with chronic obstructive pulmonary disease. Sci. Rep. 8, 1. https:\/\/doi.org\/10.1038\/s41598-018-23424-0 (2018).","journal-title":"Sci. Rep."},{"key":"20635_CR52","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2903278","author":"M Gidea","year":"2017","unstructured":"Gidea, M. Topology data analysis of critical transitions in financial networks. SSRN Electron. J. https:\/\/doi.org\/10.2139\/ssrn.2903278 (2017).","journal-title":"SSRN Electron. J."},{"key":"20635_CR53","doi-asserted-by":"publisher","unstructured":"Ramamurthi, Y. & Chattopadhyay, A. Topological shape matching using multi-dimensional reeb graphs. In Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP \u201922. https:\/\/doi.org\/10.1145\/3571600.3571606 (Association for Computing Machinery, 2023).","DOI":"10.1145\/3571600.3571606"},{"key":"20635_CR54","doi-asserted-by":"crossref","unstructured":"Giansiracusa, N., Giansiracusa, R.\u00a0S. & Moon, C. Persistent homology machine learning for fingerprint classification. In 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA) 1219\u20131226 (2017).","DOI":"10.1109\/ICMLA.2019.00201"},{"key":"20635_CR55","doi-asserted-by":"crossref","unstructured":"Edelsbrunner, H. & Harer, J. Computational Topology\u2014An Introduction (American Mathematical Society, 2010).","DOI":"10.1090\/mbk\/069"},{"key":"20635_CR56","doi-asserted-by":"publisher","first-page":"104803","DOI":"10.1016\/j.compbiomed.2021.104803","volume":"137","author":"SS BiswajitJena","year":"2021","unstructured":"BiswajitJena, S. S. et al. Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review. Comput. Biol. Med. 137, 104803. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104803 (2021).","journal-title":"Comput. Biol. Med."},{"key":"20635_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7162-y","author":"A Vijayalakshmi","year":"2019","unstructured":"Vijayalakshmi, A. & Rajesh, K. Deep learning approach to detect malaria from microscopic images. Multimedia Tools Appl. https:\/\/doi.org\/10.1007\/s11042-019-7162-y (2019).","journal-title":"Multimedia Tools Appl."},{"key":"20635_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2020.164237","author":"H Shakarami","year":"2020","unstructured":"Shakarami, H. et al. A cad system for diagnosing alzheimer\u2019s disease using 2d slices and an improved alexnet-svm method. Optik. https:\/\/doi.org\/10.1016\/j.ijleo.2020.164237 (2020).","journal-title":"Optik."},{"key":"20635_CR59","doi-asserted-by":"publisher","unstructured":"Khosro, B. et al. A hybrid deep transfer learning based approach for covid-19 classification in chest x-ray images. In Proc. International Iranian Conference on Biomedical Engineering (ICBME 2020). https:\/\/doi.org\/10.1109\/ICBME51989.2020.9319426 (2020).","DOI":"10.1109\/ICBME51989.2020.9319426"},{"key":"20635_CR60","doi-asserted-by":"publisher","first-page":"166405","DOI":"10.1016\/j.ijleo.2021.166405","volume":"231","author":"SE Ahmed","year":"2021","unstructured":"Ahmed, S. E. & Zeinab, F. E. Covidetection-net: A tailored covid-19 detection from chest radiography images using deep learning. Optik 231, 166405. https:\/\/doi.org\/10.1016\/j.ijleo.2021.166405 (2021).","journal-title":"Optik"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20635-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20635-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20635-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:33:47Z","timestamp":1761125627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20635-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["20635"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-20635-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]},"assertion":[{"value":"20 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 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":"36845"}}