{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:05:27Z","timestamp":1771956327656,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00521-025-11770-w","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T04:06:29Z","timestamp":1770869189000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Novel CNN-driven model for brain tumor image categorization"],"prefix":"10.1007","volume":"38","author":[{"given":"Essam","family":"Abdellatef","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4650-0484","authenticated-orcid":false,"given":"Alshimaa H.","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasha M.","family":"Al-Makhlasawy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"11770_CR1","doi-asserted-by":"publisher","first-page":"113050","DOI":"10.1109\/ACCESS.2023.3317796","volume":"11","author":"A Younis","year":"2023","unstructured":"Younis A, Li Q, Khalid M, Clemence B, Adamu MJ (2023) Deep learning techniques for the classification of brain tumor: a comprehensive survey. IEEE Access 11:113050\u2013113063","journal-title":"IEEE Access"},{"key":"11770_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2024.1288274","volume":"18","author":"W Chen","year":"2024","unstructured":"Chen W, Tan X, Zhang J, Du G, Fu Q, Jiang H (2024) A robust approach for multi-type classification of brain tumor using deep feature fusion. Front Neurosci 18:1288274","journal-title":"Front Neurosci"},{"key":"11770_CR3","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1867","volume":"10","author":"AA Asiri","year":"2024","unstructured":"Asiri AA, Shaf A, Ali T, Pasha MA, Khan A, Irfan M, Alqahtani S et al (2024) Advancing brain tumor detection: harnessing the Swin Transformer\u2019s power for accurate classification and performance analysis. PeerJ Comput Sci 10:e1867","journal-title":"PeerJ Comput Sci"},{"key":"11770_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106966","volume":"160","author":"MM Emam","year":"2023","unstructured":"Emam MM, Samee NA, Jamjoom MM, Houssein EH (2023) Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm. Comput Biol Med 160:106966","journal-title":"Comput Biol Med"},{"key":"11770_CR5","doi-asserted-by":"crossref","unstructured":"Zhu Z, Khan MA, Wang S-H, Zhang Y-D (2023) RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification.\u00a0Comput, Mater & Continua\u00a075, 1","DOI":"10.32604\/cmc.2023.030790"},{"key":"11770_CR6","doi-asserted-by":"publisher","first-page":"90","DOI":"10.15628\/holos.2017.6144","volume":"5","author":"N Ghanbari","year":"2017","unstructured":"Ghanbari N, Heidari M (2017) Shuffled frog leaping algorithm and feature selection for improving recognition rate of Persian handwritten digits classifier. Holos 5:90\u201398","journal-title":"Holos"},{"issue":"5","key":"11770_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/s22051960","volume":"22","author":"M Arabahmadi","year":"2022","unstructured":"Arabahmadi M, Farahbakhsh R, Rezazadeh J (2022) Deep learning for smart healthcare\u2014a survey on brain tumor detection from medical imaging. Sensors 22(5):1960","journal-title":"Sensors"},{"issue":"10","key":"11770_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12102541","volume":"12","author":"NA Samee","year":"2022","unstructured":"Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA (2022) Classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model. Diagnostics 12(10):2541","journal-title":"Diagnostics"},{"key":"11770_CR9","doi-asserted-by":"crossref","unstructured":"Reddy L, Elangovan M, Vamsikrishna M, Ravindra C (2024) Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans.\"\u00a0EAI Endorsed Transactions on Pervasive Health & Technology\u00a010, 1","DOI":"10.4108\/eetpht.10.5553"},{"issue":"2","key":"11770_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/app14020642","volume":"14","author":"M G\u00fcler","year":"2024","unstructured":"G\u00fcler M, Naml\u0131 E (2024) Brain tumor detection with deep learning methods\u2019 classifier optimization using medical images. Appl Sci 14(2):642","journal-title":"Appl Sci"},{"issue":"16","key":"11770_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15164172","volume":"15","author":"AB Abdusalomov","year":"2023","unstructured":"Abdusalomov AB, Mukhiddinov M, Whangbo TK (2023) Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers 15(16):4172","journal-title":"Cancers"},{"key":"11770_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2024.101026","volume":"31","author":"S Anantharajan","year":"2024","unstructured":"Anantharajan S, Gunasekaran S, Subramanian T (2024) MRI brain tumor detection using deep learning and machine learning approaches. Meas Sensors 31:101026","journal-title":"Meas Sensors"},{"issue":"1","key":"11770_CR13","first-page":"1","volume":"62","author":"VK Dhakshnamurthy","year":"2024","unstructured":"Dhakshnamurthy VK, Govindan M, Sreerangan K, Nagarajan MD, Thomas A (2024) Brain tumor detection and classification using transfer learning models. Eng Proc 62(1):1","journal-title":"Eng Proc"},{"issue":"5","key":"11770_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11050744","volume":"11","author":"M Masood","year":"2021","unstructured":"Masood M, Nazir T, Nawaz M, Mehmood A, Rashid J, Kwon H-Y, Mahmood T, Hussain A (2021) A novel deep learning method for recognition and classification of brain tumors from MRI images. Diagnostics 11(5):744","journal-title":"Diagnostics"},{"key":"11770_CR15","doi-asserted-by":"crossref","unstructured":"Swathi VN, Sinduja K, Kumar VR, Mahendar A, Prasad GV, Samya B. (2024) Deep learning-based brain tumor detection: An MRI segmentation approach.\" In MATEC Web of Conferences, 392, 01157. EDP Sciences,","DOI":"10.1051\/matecconf\/202439201157"},{"key":"11770_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1248452","volume":"13","author":"G Dheepak","year":"2024","unstructured":"Dheepak G, Vaishali D (2024) Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Front Oncol 13:1248452","journal-title":"Front Oncol"},{"issue":"06","key":"11770_CR17","doi-asserted-by":"publisher","first-page":"102","DOI":"10.4236\/jbise.2020.136010","volume":"13","author":"AM Sarhan","year":"2020","unstructured":"Sarhan AM (2020) Brain tumor classification in magnetic resonance images using deep learning and wavelet transform. J Biomed Sci Eng 13(06):102","journal-title":"J Biomed Sci Eng"},{"issue":"2","key":"11770_CR18","doi-asserted-by":"publisher","first-page":"4604","DOI":"10.3934\/math.2024222","volume":"9","author":"K Gasmi","year":"2024","unstructured":"Gasmi K, Kharrat A, Ben Ammar L, Ben Ltaifa I, Krichen M, Mrabet M, Alshammari H, Yahyaoui S, Khaldi K, Hrizi O (2024) Classification of MRI brain tumors based on registration preprocessing and deep belief networks. AIMS Math 9(2):4604\u20134631","journal-title":"AIMS Math"},{"issue":"1","key":"11770_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s12880-024-01195-7","volume":"24","author":"S Srinivasan","year":"2024","unstructured":"Srinivasan S, Francis D, Mathivanan SK, Rajadurai H, Shivahare BD, Shah MA (2024) A hybrid deep CNN model for brain tumor image multi-classification. BMC Med Imaging 24(1):21","journal-title":"BMC Med Imaging"},{"key":"11770_CR20","doi-asserted-by":"publisher","first-page":"23029","DOI":"10.1038\/s41598-023-50505-6","volume":"13","author":"B Babu Vimala","year":"2023","unstructured":"Babu Vimala B, Srinivasan S, Mathivanan SK, Mahalakshmi JP, Dalu GT (2023) Detection and classification of brain tumor using hybrid deep learning models. Sci Rep 13:23029","journal-title":"Sci Rep"},{"issue":"5","key":"11770_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/app14052210","volume":"14","author":"S Natha","year":"2024","unstructured":"Natha S, Laila U, Gashim IA, Mahboob K, Saeed MN, Noaman KM (2024) Automated brain tumor identification in biomedical radiology images: a multi-model ensemble deep learning approach. Appl Sci 14(5):2210","journal-title":"Appl Sci"},{"issue":"2","key":"11770_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare9020153","volume":"9","author":"FJ D\u00edaz-Pernas","year":"2021","unstructured":"D\u00edaz-Pernas FJ, Mart\u00ednez-Zarzuela M, Ant\u00f3n-Rodr\u00edguez M, Gonz\u00e1lez-Ortega D, MDPI (2021) A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare 9(2):153","journal-title":"Healthcare"},{"key":"11770_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105778","volume":"89","author":"B Sandhiya","year":"2024","unstructured":"Sandhiya B, Kanaga Suba Raja S (2024) Deep learning and optimized learning machine for brain tumor classification. Biomed Signal Process Control 89:105778","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"11770_CR25","first-page":"1339","volume":"31","author":"S Raza","year":"2024","unstructured":"Raza S, Gul N, Khattak H, Rehan A, Farid M, Kamal A, Rajput D, Mukhtiar S, Ullah A (2024) Brain tumor detection and classification using deep feature fusion and stacking concepts. J Popul Ther Clin Pharmacol 31(1):1339\u20131356","journal-title":"J Popul Ther Clin Pharmacol"},{"issue":"3","key":"11770_CR26","doi-asserted-by":"publisher","first-page":"209","DOI":"10.14419\/ijet.v7i3.27.17763","volume":"7","author":"S Mishra","year":"2018","unstructured":"Mishra S, Prakash M, Hafsa A, Anchana G (2018) ANFIS to detect brain tumor using MRI. Int. J. Eng. Technol. 7(3):209\u2013214","journal-title":"Int. J. Eng. Technol."},{"key":"11770_CR27","unstructured":"Sharma, M (2012) Artificial neural network fuzzy inference system (ANFIS) for brain tumor detection.\"\u00a0arXiv preprint arXiv:1212.0059"},{"issue":"2","key":"11770_CR28","first-page":"43","volume":"10","author":"M Wageh","year":"2023","unstructured":"Wageh M, Amin KM, Zytoon A, Ibrahim M (2023) Brain tumor detection based on a combination of GLCM and LBP features with PCA and IG. IJCI Int J Comput Inf 10(2):43\u201353","journal-title":"IJCI Int J Comput Inf"},{"issue":"20","key":"11770_CR29","doi-asserted-by":"publisher","first-page":"15975","DOI":"10.1007\/s00521-019-04679-8","volume":"32","author":"M Sharif","year":"2020","unstructured":"Sharif M, Amin J, Raza M, Anjum MA, Afzal H, Shad SA (2020) Brain tumor detection based on extreme learning. Neural Comput Appl 32(20):15975\u201315987","journal-title":"Neural Comput Appl"},{"issue":"1","key":"11770_CR30","doi-asserted-by":"publisher","first-page":"160","DOI":"10.21467\/ias.9.1.160-173","volume":"9","author":"LJ Qi","year":"2020","unstructured":"Qi LJ, Alias N (2020) Effective and efficient LDA+ ELM model for supervised classification of brain tumor types using 2D MRI scans. Int Ann Sci 9(1):160\u2013173","journal-title":"Int Ann Sci"},{"issue":"3","key":"11770_CR31","doi-asserted-by":"publisher","first-page":"203","DOI":"10.14257\/ijbsbt.2016.8.3.21","volume":"8","author":"S Roy","year":"2016","unstructured":"Roy S, Sadhu S, Bandyopadhyay SK, Bhattacharyya D, Kim T-H (2016) Brain tumor classification using adaptive neuro-fuzzy inference system from MRI. Int J Bio-Sci Bio-Technol 8(3):203\u2013218","journal-title":"Int J Bio-Sci Bio-Technol"},{"issue":"2","key":"11770_CR32","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1002\/ima.22170","volume":"26","author":"P Thirumurugan","year":"2016","unstructured":"Thirumurugan P, Shanthakumar P (2016) Brain tumor detection and diagnosis using ANFIS classifier. Int J Imaging Syst Technol 26(2):157\u2013162","journal-title":"Int J Imaging Syst Technol"},{"key":"11770_CR33","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2023.029959","author":"S Keerthi","year":"2023","unstructured":"Keerthi S, Santhi P (2023) Precise multi-class classification of brain tumor via optimization based relevance vector machine. Intell Autom & Soft Comput. https:\/\/doi.org\/10.32604\/iasc.2023.029959","journal-title":"Intell Autom & Soft Comput"},{"issue":"5","key":"11770_CR34","first-page":"2395","volume":"15","author":"A Dixit","year":"2023","unstructured":"Dixit A, Thakur MK (2023) RVM-MR image brain tumour classification using novel statistical feature extractor. Int J Inf Technol 15(5):2395\u20132407","journal-title":"Int J Inf Technol"},{"issue":"11-12","key":"11770_CR35","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1177\/09544119241293007","volume":"238","author":"AK Verma","year":"2024","unstructured":"Verma AK, Saurabh P, Shah DM, Inturi V, Sudha R, Rajasekharan SG, Soundrapandiyan R (2024) A wavelet and local binary pattern-based feature descriptor for the detection of chronic infection through thoracic X-ray images. Proc Inst Mech Eng [H] 238(11\u201312):1133\u20131145","journal-title":"Proc Inst Mech Eng [H]"},{"issue":"6","key":"11770_CR36","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1080\/03091902.2024.2438158","volume":"48","author":"A Badkul","year":"2024","unstructured":"Badkul A, Inturi V, Sudha R (2024) Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning. J Med Eng Technol 48(6):213\u2013222","journal-title":"J Med Eng Technol"},{"key":"11770_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115650","volume":"185","author":"AK Verma","year":"2021","unstructured":"Verma AK, Vamsi I, Saurabh P, Sudha R, Gr S (2021) Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing. Expert Syst Appl 185:115650","journal-title":"Expert Syst Appl"},{"key":"11770_CR38","doi-asserted-by":"crossref","unstructured":"Ma Z, Lin Z (2023) The classification of human brain tumors with machine learning.\" In: Journal of Physics: Conference Series, IOP Publishing 2580, 1, 012033","DOI":"10.1088\/1742-6596\/2580\/1\/012033"},{"key":"11770_CR39","doi-asserted-by":"crossref","unstructured":"Tawhid A, Teotia T, Elmiligi H (2021) Machine learning for optimizing healthcare resources.\" In Machine Learning, Big Data, and IoT for Medical Informatics, 215\u2013239. Academic Press","DOI":"10.1016\/B978-0-12-821777-1.00020-3"},{"issue":"1","key":"11770_CR40","first-page":"12","volume":"2","author":"M Settles","year":"2005","unstructured":"Settles M (2005) An introduction to particle swarm optimization. Dep. Comput Sci, Univ Idaho 2(1):12","journal-title":"Dep. Comput Sci, Univ Idaho"},{"key":"11770_CR41","doi-asserted-by":"crossref","unstructured":"Sharma H, Hazrati G, Bansal JC (2018) Spider monkey optimization algorithm.\" In Evolutionary and swarm intelligence algorithms, Cham: Springer International Publishing 43\u201359","DOI":"10.1007\/978-3-319-91341-4_4"},{"key":"11770_CR42","unstructured":"https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset?resource=download"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11770-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11770-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11770-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:16:31Z","timestamp":1771953391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11770-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11770"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11770-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"6 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"41"}}