{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:36:03Z","timestamp":1779294963248,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"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":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00852-1","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T13:58:18Z","timestamp":1747663098000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computational Brain Imaging Framework for Neurological Mapping and Disorder Classification Using Multimodal Image Processing"],"prefix":"10.1007","volume":"18","author":[{"given":"S.","family":"Karthikeyan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Muthu Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. L.","family":"Kiran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Srivatsan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"852_CR1","doi-asserted-by":"crossref","unstructured":"Mahmood, H., Islam, S.M.S., Iqbal, A.: Multimodal 3D image registration for mapping brain disorders. bioRxiv, 2024-08 (2024)","DOI":"10.1101\/2024.08.24.609508"},{"key":"852_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101768","volume":"65","author":"I Mhiri","year":"2020","unstructured":"Mhiri, I., Khalifa, A.B., Mahjoub, M.A., Rekik, I.: Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning. Med. Image Anal. 65, 101768 (2020)","journal-title":"Med. Image Anal."},{"key":"852_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, S., Xia, K., Jiang, Y., Qian, P., Alzheimer\u2019s Disease Neuroimaging Initiative: Alzheimer\u2019s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. Inform. Fus. 66, 170\u2013183 (2021)","DOI":"10.1016\/j.inffus.2020.09.002"},{"key":"852_CR4","doi-asserted-by":"crossref","unstructured":"Lin, W., Lin, W., Chen, G., Zhang, H., Gao, Q., Huang, Y., Alzheimer\u2019s Disease Neuroimaging Initiative: Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer\u2019s disease. Front. Neurosci. 15, 646013 (2021)","DOI":"10.3389\/fnins.2021.646013"},{"key":"852_CR5","doi-asserted-by":"crossref","unstructured":"Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadehsani, R., Gorriz, J.M.: Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: a review. Inform. Fus. 93, 85\u2013117 (2023)","DOI":"10.1016\/j.inffus.2022.12.010"},{"issue":"5","key":"852_CR6","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1007\/s10462-019-09766-9","volume":"53","author":"KR Bhatele","year":"2020","unstructured":"Bhatele, K.R., Bhadauria, S.S.: Brain structural disorders detection and classification approaches: a review. Artif. Intell. Rev.. Intell. Rev. 53(5), 3349\u20133401 (2020)","journal-title":"Artif. Intell. Rev.. Intell. Rev."},{"key":"852_CR7","doi-asserted-by":"crossref","unstructured":"Shou, G., Yuan, H., Ding, L.: Mapping brain networks using multimodal data. In: Handbook of Neuroengineering, pp. 2975\u20133025 (2023)","DOI":"10.1007\/978-981-16-5540-1_83"},{"issue":"2","key":"852_CR8","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1002\/hbm.26077","volume":"44","author":"MA Rahaman","year":"2023","unstructured":"Rahaman, M.A., Chen, J., Fu, Z., Lewis, N., Iraji, A., van Erp, T.G., Calhoun, V.D.: Deep multimodal predictome for studying mental disorders. Hum. Brain Mapp. 44(2), 509\u2013522 (2023)","journal-title":"Hum. Brain Mapp."},{"key":"852_CR9","doi-asserted-by":"crossref","unstructured":"Hu, W., Meng, X., Bai, Y., Zhang, A., Qu, G., Cai, B., Wang, Y.P.: Interpretable multimodal fusion networks reveal mechanisms of brain cognition. IEEE Trans. Med. Imaging 40(5), 1474\u20131483 (2021)","DOI":"10.1109\/TMI.2021.3057635"},{"key":"852_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106721","volume":"97","author":"T Mahmood","year":"2024","unstructured":"Mahmood, T., Rehman, A., Saba, T., Wang, Y., Alamri, F.S.: Alzheimer\u2019s disease unveiled: cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis. Biomed. Signal Process. Control 97, 106721 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"852_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101813","volume":"104","author":"MP Hosseini","year":"2020","unstructured":"Hosseini, M.P., Tran, T.X., Pompili, D., Elisevich, K., Soltanian-Zadeh, H.: Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing. Artif. Intell. Med.. Intell. Med. 104, 101813 (2020)","journal-title":"Artif. Intell. Med.. Intell. Med."},{"key":"852_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102475","volume":"136","author":"SQ Abbas","year":"2023","unstructured":"Abbas, S.Q., Chi, L., Chen, Y.P.P.: Deepmnf: deep multimodal neuroimaging framework for diagnosing autism spectrum disorder. Artif. Intell. Med.. Intell. Med. 136, 102475 (2023)","journal-title":"Artif. Intell. Med.. Intell. Med."},{"key":"852_CR13","doi-asserted-by":"publisher","first-page":"779","DOI":"10.3389\/fnins.2020.00779","volume":"14","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Wang, M., Liu, M., Zhang, D.: A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci.Neurosci. 14, 779 (2020)","journal-title":"Front. Neurosci.Neurosci."},{"issue":"17","key":"852_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26783","volume":"45","author":"Y Bi","year":"2024","unstructured":"Bi, Y., Abrol, A., Fu, Z., Calhoun, V.D.: A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Hum. Brain Mapp. 45(17), e26783 (2024)","journal-title":"Hum. Brain Mapp."},{"key":"852_CR15","doi-asserted-by":"crossref","unstructured":"Safai, A., Vakharia, N., Prasad, S., Saini, J., Shah, A., Lenka, A., Ingalhalikar, M.: Multimodal brain connectomics-based prediction of Parkinson\u2019s disease using graph attention networks. Front. Neurosci. 15, 741489 (2022)","DOI":"10.3389\/fnins.2021.741489"},{"key":"852_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103500","volume":"74","author":"N Goenka","year":"2022","unstructured":"Goenka, N., Tiwari, S.: AlzVNet: a volumetric convolutional neural network for multiclass classification of Alzheimer\u2019s disease through multiple neuroimaging computational approaches. Biomed. Signal Process. Control 74, 103500 (2022)","journal-title":"Biomed. Signal Process. Control"},{"issue":"8","key":"852_CR17","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.3390\/diagnostics11081402","volume":"11","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Li, G., Xu, Y., Tang, X.: Application of artificial intelligence in the MRI classification task of human brain neurological and psychiatric diseases: a scoping review. Diagnostics 11(8), 1402 (2021)","journal-title":"Diagnostics"},{"key":"852_CR18","doi-asserted-by":"crossref","unstructured":"Qiu, S., Joshi, P.S., Miller, M.I., Xue, C., Zhou, X., Karjadi, C., Kolachalama, V.B.: Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification. Brain 143(6), 1920\u20131933 (2020)","DOI":"10.1093\/brain\/awaa137"},{"issue":"1","key":"852_CR19","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/JBHI.2021.3097721","volume":"26","author":"X Gao","year":"2021","unstructured":"Gao, X., Shi, F., Shen, D., Liu, M.: Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in Alzheimer\u2019s disease. IEEE J. Biomed. Health Inform. 26(1), 36\u201343 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"852_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, H., He, L., Zhang, Y., Shen, L., Chen, B.: Interpretable graph convolutional network of multi-modality brain imaging for Alzheimer\u2019s disease diagnosis. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135 (2022)","DOI":"10.1109\/ISBI52829.2022.9761449"},{"issue":"1","key":"852_CR21","doi-asserted-by":"publisher","first-page":"5210","DOI":"10.1038\/s41598-024-56001-9","volume":"14","author":"G Castellano","year":"2024","unstructured":"Castellano, G., Esposito, A., Lella, E., Montanaro, G., Vessio, G.: Automated detection of Alzheimer\u2019s disease: a multi-modal approach with 3D MRI and amyloid PET. Sci. Rep. 14(1), 5210 (2024)","journal-title":"Sci. Rep."},{"key":"852_CR22","doi-asserted-by":"crossref","unstructured":"Mandal, P.K., Jindal, K., Roy, S., Arora, Y., Sharma, S., Joon, S., Shandilya, S.: SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform. Front. Neurol. 14, 1258116 (2023)","DOI":"10.3389\/fneur.2023.1258116"},{"key":"852_CR23","doi-asserted-by":"crossref","unstructured":"Ahilan, A., Anlin Sahaya Tinu, M., Jasmine Gnana Malar, A., Muthu Kumar, B.: Stationary wavelet-oriented luminance enhancement approach for brain tumor detection with multi-modality images. In: International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp. 461\u2013473 (2023)","DOI":"10.1007\/978-981-99-6702-5_38"},{"issue":"03","key":"852_CR24","doi-asserted-by":"publisher","first-page":"2250030","DOI":"10.1142\/S0219477522500304","volume":"21","author":"R Sundarasekar","year":"2022","unstructured":"Sundarasekar, R., Appathurai, A.: Automatic brain tumor detection and classification based on IoT and machine learning techniques. Fluctuat. Noise Lett. 21(03), 2250030 (2022)","journal-title":"Fluctuat. Noise Lett."},{"key":"852_CR25","doi-asserted-by":"crossref","unstructured":"Rajeswari, D., Rajendran, S., Arivarasi, A., Govindasamy, A., Ahilan, A.: TOSS: deep learning based track object detection using smart sensor. IEEE Sens. J. 2024, 1. (2024)","DOI":"10.1109\/JSEN.2024.3447730"},{"issue":"01","key":"852_CR26","first-page":"20","volume":"01","author":"DS Dakshina","year":"2023","unstructured":"Dakshina, D.S., Jayapriya, P., Kala, R.: Saree texture analysis and classification via deep learning framework. Int. J. Data Sci. Artif. Intell. 01(01), 20\u201325 (2023)","journal-title":"Int. J. Data Sci. Artif. Intell."},{"key":"852_CR27","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.comnet.2019.01.028","volume":"151","author":"E Fenil","year":"2019","unstructured":"Fenil, E., Manogaran, G., Vivekananda, G.N., Thanjaivadivel, T., Jeeva, S., Ahilan, A.J.C.N.: Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Comput. Netw.. Netw. 151, 191\u2013200 (2019)","journal-title":"Comput. Netw.. Netw."},{"issue":"10","key":"852_CR28","first-page":"829","volume":"15","author":"P Whitin","year":"2024","unstructured":"Whitin, P., Sivakumar, S., Geetha, M., Devaki, M., Bhuvanesh, A., Balasubramaniyan, K., Ahilan, A.: Mask FORD-NET: efficient detection of digital image forgery using hybrid REG-NET based mask-RCNN. Int. J. Electr. Comput. Eng. Syst. 15(10), 829\u2013835 (2024)","journal-title":"Int. J. Electr. Comput. Eng. Syst."},{"issue":"10","key":"852_CR29","doi-asserted-by":"publisher","first-page":"7419","DOI":"10.1007\/s11760-024-03404-w","volume":"18","author":"NG Rani","year":"2024","unstructured":"Rani, N.G., Priya, N.H., Ahilan, A., Muthukumaran, N.: LV-YOLO: logistic vehicle speed detection and counting using deep learning-based YOLO network. SIViP 18(10), 7419\u20137429 (2024)","journal-title":"SIViP"},{"issue":"1","key":"852_CR30","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11760-023-02710-z","volume":"18","author":"M Karthikeyan","year":"2024","unstructured":"Karthikeyan, M., Subashini, T.S., Srinivasan, R., Santhanakrishnan, C., Ahilan, A.: YOLOAPPLE: augment Yolov3 deep learning algorithm for apple fruit quality detection. SIViP 18(1), 119\u2013128 (2024)","journal-title":"SIViP"},{"issue":"5","key":"852_CR31","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.3390\/app10051894","volume":"10","author":"L Lazli","year":"2020","unstructured":"Lazli, L., Boukadoum, M., Mohamed, O.A.: A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on Alzheimer disease diagnosis and the contribution of the multimodal fusion. Appl. Sci. 10(5), 1894 (2020)","journal-title":"Appl. Sci."},{"key":"852_CR32","doi-asserted-by":"publisher","first-page":"6149","DOI":"10.1016\/j.csbj.2022.11.008","volume":"20","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Tang, S., Ma, R., Zamit, I., Wei, Y., Pan, Y.: Multi-modal intermediate integrative methods in neuropsychiatric disorders: a review. Comput. Struct. Biotechnol. J.. Struct. Biotechnol. J. 20, 6149\u20136162 (2022)","journal-title":"Comput. Struct. Biotechnol. J.. Struct. Biotechnol. J."},{"issue":"3","key":"852_CR33","doi-asserted-by":"publisher","first-page":"469","DOI":"10.3390\/biology11030469","volume":"11","author":"AA Lima","year":"2022","unstructured":"Lima, A.A., Mridha, M.F., Das, S.C., Kabir, M.M., Islam, M.R., Watanobe, Y.: A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology 11(3), 469 (2022)","journal-title":"Biology"},{"issue":"2","key":"852_CR34","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1093\/bib\/bbaa310","volume":"22","author":"N Burgos","year":"2021","unstructured":"Burgos, N., Bottani, S., Faouzi, J., Thibeau-Sutre, E., Colliot, O.: Deep learning for brain disorders: from data processing to disease treatment. Brief. Bioinform.Bioinform. 22(2), 1560\u20131576 (2021)","journal-title":"Brief. Bioinform.Bioinform."},{"key":"852_CR35","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2021.742807","volume":"15","author":"SS Menon","year":"2021","unstructured":"Menon, S.S., Krishnamurthy, K.: Multimodal ensemble deep learning to predict disruptive behavior disorders in children. Front. Neuroinform.Neuroinform. 15, 742807 (2021)","journal-title":"Front. Neuroinform.Neuroinform."},{"key":"852_CR36","doi-asserted-by":"crossref","unstructured":"Bushra, U.H., Priya, F.C., Patwary, M.J.A.: Multi-modal feature fusion with fuzziness-based semi-supervised learning for Alzheimer\u2019s disease diagnosis. In: 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), pp. 1\u20136 (2024)","DOI":"10.1109\/COMPAS60761.2024.10796811"},{"key":"852_CR37","doi-asserted-by":"crossref","unstructured":"Liu, J., Du, H., Mao, J., Zhu, J., Tian, X.: A novel dual interactive network for Parkinson\u2019s disease diagnosis based on multi-modality magnetic resonance imaging. In: International Symposium on Bioinformatics Research and Applications, pp. 434\u2013444. Springer, Singapore (2024)","DOI":"10.1007\/978-981-97-5131-0_37"},{"issue":"2","key":"852_CR38","doi-asserted-by":"publisher","first-page":"3767","DOI":"10.1007\/s11042-023-15738-7","volume":"83","author":"DA Arafa","year":"2024","unstructured":"Arafa, D.A., Moustafa, H.E.D., Ali, H.A., Ali-Eldin, A.M., Saraya, S.F.: A deep learning framework for early diagnosis of Alzheimer\u2019s disease on MRI images. Multimed. Tools Appl. 83(2), 3767\u20133799 (2024)","journal-title":"Multimed. Tools Appl."},{"key":"852_CR39","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.procs.2024.04.023","volume":"235","author":"S Desai","year":"2024","unstructured":"Desai, S., Chhinkaniwala, H., Shah, S., Gajjar, P.: Enhancing Parkinson\u2019s disease diagnosis through deep learning-based classification of 3D MRI images. Proc. Comput. Sci. 235, 201\u2013213 (2024)","journal-title":"Proc. Comput. Sci."},{"key":"852_CR40","doi-asserted-by":"publisher","first-page":"1323623","DOI":"10.3389\/fneur.2024.1323623","volume":"15","author":"L Yang","year":"2024","unstructured":"Yang, L., Peng, B., Gao, W., Liu, Y., Liang, J., Zhu, M., Hu, H., Lu, Z., Pang, C., Dai, Y., Sun, Y.: Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning. Front. Neurol. 15, 1323623 (2024)","journal-title":"Front. Neurol."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00852-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00852-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00852-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T13:58:25Z","timestamp":1747663105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00852-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["852"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00852-1","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,19]]},"assertion":[{"value":"8 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 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":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"My research guide reviewed and ethically approved this manuscript for publishing in this journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}},{"value":"I certify that I have explained the nature and purpose of this study to the above-named individual, and I have discussed the potential benefits of this study participation. The questions the individual had about this study have been answered, and we will always be available to address future questions.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"121"}}