{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:09:25Z","timestamp":1780322965547,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["G: 219 \u2013 142-1443"],"award-info":[{"award-number":["G: 219 \u2013 142-1443"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s12559-022-10096-2","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T08:03:21Z","timestamp":1673597001000},"page":"2036-2046","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Tumor Localization and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm"],"prefix":"10.1007","volume":"16","author":[{"given":"Jaber","family":"Alyami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad","family":"Almutairi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul Muiz","family":"Fayyaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudipta","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alhassan","family":"Alkhurim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"10096_CR1","doi-asserted-by":"publisher","unstructured":"Sun W, Dai GZ, Zhang XR, He XZ, Chen X. TBE-Net: a three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification. IEEE Transact Intell Transp Syst. 2021;pp. 1\u201313. https:\/\/doi.org\/10.1109\/TITS.2021.3130403.","DOI":"10.1109\/TITS.2021.3130403"},{"key":"10096_CR2","doi-asserted-by":"crossref","unstructured":"Khanna P, Tanveer M, Prasad M, Lin CT.\u00a0Artificial intelligence and deep learning for biomedical applications. Multimed Tools Appl. 2022;81:13137.","DOI":"10.1007\/s11042-022-12956-3"},{"key":"10096_CR3","doi-asserted-by":"crossref","unstructured":"Dipu NM, Shohan SA, Salam K. Deep learning based brain tumor detection and classification. Int Conf Intell Technol (CONIT), IEEE.\u00a02021;pp. 1\u20136.","DOI":"10.1109\/CONIT51480.2021.9498384"},{"key":"10096_CR4","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","volume":"131","author":"A Tiwari","year":"2020","unstructured":"Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recogn Lett. 2020;131:244\u201360.","journal-title":"Pattern Recogn Lett"},{"issue":"8","key":"10096_CR5","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.3390\/sym12081256","volume":"12","author":"HA Khalil","year":"2020","unstructured":"Khalil HA, Darwish S, Ibrahim YM, Hassan OF. 3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm. Symmetry. 2020;12(8):1256.","journal-title":"Symmetry"},{"key":"10096_CR6","doi-asserted-by":"crossref","unstructured":"Carlo R, Renato C, Giuseppe C, Lorenzo U,\u00a0et al. Distinguishing functional from non-functional pituitary macroadenomas with a machine learning analysis. Mediterr Conf Med Biol Eng Comput. 2019;pp. 1822\u20131829.","DOI":"10.1007\/978-3-030-31635-8_221"},{"key":"10096_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109684","volume":"139","author":"A \u00c7inar","year":"2020","unstructured":"\u00c7inar A, Yildirim M. Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses. 2020;139: 109684.","journal-title":"Med Hypotheses"},{"key":"10096_CR8","doi-asserted-by":"publisher","first-page":"92615","DOI":"10.1109\/ACCESS.2019.2927433","volume":"7","author":"K Hu","year":"2019","unstructured":"Hu K, Gan Q, Zhang Y, Deng S, Xiao F, et al. Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access. 2019;7:92615\u201329.","journal-title":"IEEE Access"},{"key":"10096_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103758","volume":"121","author":"MA Naser","year":"2020","unstructured":"Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med. 2020;121: 103758.","journal-title":"Comput Biol Med"},{"issue":"1","key":"10096_CR10","doi-asserted-by":"publisher","first-page":"271","DOI":"10.21873\/anticanres.13949","volume":"40","author":"V Romeo","year":"2020","unstructured":"Romeo V, Cuocolo R, Ricciardi C, Ugga L, Cocozza S, et al. Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach. Anticancer Res. 2020;40(1):271\u201380.","journal-title":"Anticancer Res"},{"key":"10096_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.patrec.2019.11.019","volume":"129","author":"MI Sharif","year":"2020","unstructured":"Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recogn Lett. 2020;129:181\u20139.","journal-title":"Pattern Recogn Lett"},{"issue":"10","key":"10096_CR12","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.3390\/app10103429","volume":"10","author":"V Rajinikanth","year":"2020","unstructured":"Rajinikanth V, Joseph Raj AN, Thanaraj KP, Naik GR. A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection. Appl Sci. 2020;10(10):3429.","journal-title":"Appl Sci"},{"issue":"4","key":"10096_CR13","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1109\/JBHI.2021.3083187","volume":"26","author":"I Beheshti","year":"2021","unstructured":"Beheshti I, Ganaie MA, Paliwal V, Rastogi A, et al. Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J Biomed Health Inform. 2021;26(4):1432\u201340.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"10096_CR14","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1002\/jemt.23694","volume":"84","author":"AR Khan","year":"2021","unstructured":"Khan AR, Khan S, Harouni M, Abbasi R, Iqbal S, Mehmood Z. Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification. Microsc Res Tech. 2021;84(7):1389\u201399.","journal-title":"Microsc Res Tech"},{"key":"10096_CR15","doi-asserted-by":"crossref","unstructured":"Gull S, Akbar S, Khan HU.\u00a0Automated detection of brain tumor through magnetic resonance images using convolutional neural network. BioMed Res Int.\u00a02021.","DOI":"10.1155\/2021\/3365043"},{"key":"10096_CR16","doi-asserted-by":"crossref","unstructured":"Ottom MA, Rahman HA, Dinov ID.\u00a0Znet: deep learning approach for 2D MRI brain tumor segmentation. IEEE J Transl Eng Health Med. 2022.","DOI":"10.1109\/JTEHM.2022.3176737"},{"issue":"3","key":"10096_CR17","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/JBHI.2021.3100758","volume":"26","author":"A Sekhar","year":"2021","unstructured":"Sekhar A, Biswas S, Hazra R, Sunaniya AK, Mukherjee A, Yang L. Brain tumor classification using fine-tuned GoogLeNet features and machine learning algorithms: IoMT enabled CAD system. IEEE J Biomed Health Inform. 2021;26(3):983\u201391.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"10096_CR18","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1109\/TNB.2015.2450365","volume":"14","author":"L Sallemi","year":"2015","unstructured":"Sallemi L, Njeh I, Lehericy S. Towards a computer aided prognosis for brain glioblastomas tumor growth estimation. IEEE Trans Nanobiosci. 2015;14(7):727\u201333.","journal-title":"IEEE Trans Nanobiosci"},{"issue":"6","key":"10096_CR19","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390.","journal-title":"Commun ACM"},{"key":"10096_CR20","doi-asserted-by":"crossref","unstructured":"Sun W, Dai L, Zhang X, Chang P, He X.\u00a0RSOD: real-time small object detection algorithm in UAV-based traffic monitoring. Appl Intell. 2021;pp. 1\u201316.","DOI":"10.1007\/s10489-021-02893-3"},{"key":"10096_CR21","doi-asserted-by":"crossref","unstructured":"Yar H, Hussain T, Agarwal M, Khan ZA, Gupta SK, Baik SW. Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark. IEEE Transact Image Process. 2022;31:6331-43.","DOI":"10.1109\/TIP.2022.3207006"},{"key":"10096_CR22","doi-asserted-by":"publisher","first-page":"697","DOI":"10.3390\/healthcare10040697","volume":"10","author":"I Abunadi","year":"2022","unstructured":"Abunadi I, Albraikan AA, Alzahrani JS, Eltahir MM, Hilal AM, Eldesouki MI, Motwakel A, Yaseen I. An automated glowworm swarm optimization with an inception-based deep convolutional neural network for COVID-19 diagnosis and classification. Healthcare. 2022;10:697.","journal-title":"Healthcare"},{"key":"10096_CR23","doi-asserted-by":"crossref","unstructured":"Ijaz MF, Attique M, Son Y. Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors. 2020(10):2809.","DOI":"10.3390\/s20102809"},{"issue":"1","key":"10096_CR24","first-page":"1195","volume":"72","author":"I Abunadi","year":"2022","unstructured":"Abunadi I, Althobaiti MM, Al-Wesabi FN, Hilal AM, Medani M, et al. Federated learning with blockchain assisted image classification for clustered UAV networks. Comput mater contin. 2022;72(1):1195\u2013212.","journal-title":"Comput mater contin"},{"issue":"1","key":"10096_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.5455\/aim.2020.28.29-36","volume":"28","author":"MF Safdar","year":"2020","unstructured":"Safdar MF, Alkobaisi SS, Zahra FT. A comparative analysis of data augmentation approaches for magnetic resonance imaging (MRI) scan images of brain tumor. Acta informatica medica. 2020;28(1):29.","journal-title":"Acta informatica medica"},{"issue":"11","key":"10096_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1453-8","volume":"43","author":"J Amin","year":"2019","unstructured":"Amin J, Sharif M, Yasmin M, Saba T, Anjum MA, Fernandes SL. A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. J Med Syst. 2019;43(11):1\u201316.","journal-title":"J Med Syst"},{"key":"10096_CR27","doi-asserted-by":"crossref","unstructured":"Mohan R, Ganapathy K, Rama A. Brain tumour classification of magnetic resonance images using a novel CNN based medical image analysis and detection network in comparison with VGG16. J Popul Ther Clin Pharmacol. 2021;28(2).","DOI":"10.47750\/jptcp.2022.873"},{"key":"10096_CR28","doi-asserted-by":"publisher","first-page":"116942","DOI":"10.1109\/ACCESS.2021.3105874","volume":"9","author":"MS Majib","year":"2021","unstructured":"Majib MS, Rahman MM, Sazzad TS, Khan NI, Dey SK. VGG-SCNet: a VGG Net-based deep learning framework for brain tumor detection on MRI images. IEEE Access. 2021;9:116942\u201352.","journal-title":"IEEE Access"},{"key":"10096_CR29","doi-asserted-by":"crossref","unstructured":"Too J, Abdullah AR, Mohd Saad N. Binary competitive swarm optimizer approaches for feature selection. Computation. 2019;7(2):31.","DOI":"10.3390\/computation7020031"},{"key":"10096_CR30","unstructured":"Chakrabarty N. Brain tumor dataset. [Online]. Retrieved February 7, 2022, from\u00a0https:\/\/www.kaggle.com\/navoneel\/brain-mri-images-for-brain-tumor-detection."},{"key":"10096_CR31","doi-asserted-by":"crossref","unstructured":"Saxena P, Maheshwari A, Maheshwari S. Predictive modeling of brain tumor: a deep learning approach. In Innovations in computational intelligence and computer vision. 2021;pp. 275-285. Springer, Singapore.","DOI":"10.1007\/978-981-15-6067-5_30"},{"key":"10096_CR32","unstructured":"Rai S, Chowdhury S, Sarkar S, Chowdhury K, Singh KP. A hybrid approach to brain tumor detection from MRI images using computer vision. J Innov Comput Sci Eng. 2019;8(2):8-12."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10096-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-022-10096-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10096-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T09:44:12Z","timestamp":1720172652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-022-10096-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,13]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10096"],"URL":"https:\/\/doi.org\/10.1007\/s12559-022-10096-2","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,13]]},"assertion":[{"value":"12 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}