{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T01:48:05Z","timestamp":1782784085786,"version":"3.54.5"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s11227-020-03572-9","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T21:03:13Z","timestamp":1609794193000},"page":"7236-7252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":128,"title":["Classification of brain tumors from MR images using deep transfer learning"],"prefix":"10.1007","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9395-4465","authenticated-orcid":false,"given":"\u00d6zlem","family":"Polat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4158-8421","authenticated-orcid":false,"given":"Cahfer","family":"G\u00fcngen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"3572_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.fcij.2017.12.001","volume":"3","author":"H Mohsen","year":"2018","unstructured":"Mohsen H, El-Dahshan EA, El-Horbaty EM, Salem AM (2018) Classification using deep learning neural networks for brain tumors. Future Computing Inform J 3:68\u201371. https:\/\/doi.org\/10.1016\/j.fcij.2017.12.001","journal-title":"Future Computing Inform J"},{"issue":"3","key":"3572_CR2","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s10044-017-0597-8","volume":"20","author":"K Usman","year":"2017","unstructured":"Usman K, Rajpoot K (2017) Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Appl 20(3):871\u2013881. https:\/\/doi.org\/10.1007\/s10044-017-0597-8","journal-title":"Pattern Anal Appl"},{"issue":"10","key":"3572_CR3","doi-asserted-by":"publisher","first-page":"e0140381","DOI":"10.1371\/journal.pone.0140381","volume":"10","author":"J Cheng","year":"2015","unstructured":"Cheng J, Huang W, Cao S, Yang R, Yang W et al (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One 10(10):e0140381. https:\/\/doi.org\/10.1371\/journal.pone.0140381","journal-title":"PLoS One"},{"key":"3572_CR4","first-page":"252","volume-title":"IEEE international conference on electro\/information technology; Rochester","author":"MR Ismael","year":"2018","unstructured":"Ismael MR, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. IEEE International Conference on Electro\/Information Technology; Rochester. MI, USA, pp 252\u2013257"},{"issue":"2","key":"3572_CR5","first-page":"177","volume":"10","author":"VPGP Rathi","year":"2015","unstructured":"Rathi VPGP, Palani S (2015) Brain tumor detection and classification using deep learning classifier on MRI images. Res J Appl Sci Eng Technol 10(2):177\u2013187","journal-title":"Res J Appl Sci Eng Technol"},{"key":"3572_CR6","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo M, Baloglu UB, Y\u0131ld\u0131r\u0131m \u00d6, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Sys Res 54:176\u2013188. https:\/\/doi.org\/10.1016\/j.cogsys.2018.12.007","journal-title":"Cognitive Sys Res"},{"issue":"5","key":"3572_CR7","doi-asserted-by":"publisher","first-page":"6203","DOI":"10.3934\/mbe.2020328","volume":"17","author":"HA Khan","year":"2020","unstructured":"Khan HA, Jue W, Mushtaq M, Mushtaq MU (2020) Brain tumor classification in MRI using convolutional neural network. Math Biosci Eng 17(5):6203\u20136216","journal-title":"Math Biosci Eng"},{"key":"3572_CR8","doi-asserted-by":"publisher","unstructured":"Cheng J (2017) Figshare brain tumor dataset, https:\/\/doi.org\/10.6084\/m9.figshare.1512427.v5. Accessed 12 August 2020","DOI":"10.6084\/m9.figshare.1512427.v5"},{"key":"3572_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-9035-6_33","author":"N Abiwinanda","year":"2018","unstructured":"Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2018) Brain tumor classification using convolutional neural network. Springer World Congr Med Phys Biomed Eng. https:\/\/doi.org\/10.1007\/978-981-10-9035-6_33","journal-title":"Springer World Congr Med Phys Biomed Eng"},{"key":"3572_CR10","doi-asserted-by":"crossref","unstructured":"Afshar P, Plataniotis KN, Mohammadi A (2018) Capsule networks for brain tumor classifications based on MRI images and course tumor boundaries. IEEE International Conference on Acoustics. Speech and Signal Processing; Toronto, ON, Canada, pp. 1368\u20131372","DOI":"10.1109\/ICASSP.2019.8683759"},{"key":"3572_CR11","doi-asserted-by":"crossref","unstructured":"Pashaei A, Sajedi H, Jazayeri N (2018). Brain tumor classification via convolutional neural network and extreme learning machines. In: IEEE 8th International Conference on Computer and Knowledge Engineering, Mashhad, Iran. pp. 314\u2013319","DOI":"10.1109\/ICCKE.2018.8566571"},{"key":"3572_CR12","unstructured":"Phaye SSR, Sikka A, Dhall A, Bathula DR (2018) Dense and diverse capsule networks: making the capsules learn better. http:\/\/arxiv.org\/abs\/abs\/1805.04001arXiv:abs\/1805.04001"},{"issue":"3","key":"3572_CR13","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.13005\/bpj\/1511","volume":"11","author":"J Seetha","year":"2018","unstructured":"Seetha J, Selvakumar Raja S (2018) Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 11(3):1457\u20131461. https:\/\/doi.org\/10.13005\/bpj\/1511","journal-title":"Biomed Pharmacol J"},{"issue":"4","key":"3572_CR14","doi-asserted-by":"publisher","first-page":"337","DOI":"10.31803\/tg-20190712095507","volume":"13","author":"E Av\u015far","year":"2019","unstructured":"Av\u015far E, Sal\u00e7\u0131n K (2019) Detection and classification of brain tumours from MRI images using faster R-CNN. Tehni\u010dki Glasnik 13(4):337\u2013342. https:\/\/doi.org\/10.31803\/tg-20190712095507","journal-title":"Tehni\u010dki Glasnik"},{"key":"3572_CR15","first-page":"208","volume-title":"Holistic brain tumor screening and classification based on densenet and recurrent neural network glioma multiple sclerosis stroke and traumatic brain injuries","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Li Z, Zhu H, Chen C, Gao M et al (2019) Holistic brain tumor screening and classification based on densenet and recurrent neural network glioma multiple sclerosis stroke and traumatic brain injuries. Springer International Publishing, Brainlesion, pp 208\u2013217"},{"issue":"1","key":"3572_CR16","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bbe.2018.10.004","volume":"39","author":"AK Anaraki","year":"2019","unstructured":"Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocyber Biomed Eng 39(1):63\u201374. https:\/\/doi.org\/10.1016\/j.bbe.2018.10.004","journal-title":"Biocyber Biomed Eng"},{"key":"3572_CR17","doi-asserted-by":"publisher","first-page":"36266","DOI":"10.1109\/ACCESS.2019.2904145","volume":"7","author":"A Gumaei","year":"2019","unstructured":"Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266\u201336273. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904145","journal-title":"IEEE Access"},{"key":"3572_CR18","doi-asserted-by":"publisher","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","volume":"7","author":"HH Sultan","year":"2019","unstructured":"Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215\u201369225. https:\/\/doi.org\/10.1109\/ACCESS.2019.2919122","journal-title":"IEEE Access"},{"key":"3572_CR19","doi-asserted-by":"publisher","first-page":"103345","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103345","journal-title":"Comput Biol Med"},{"key":"3572_CR20","doi-asserted-by":"publisher","first-page":"109696","DOI":"10.1016\/j.mehy.2020.109696","volume":"139","author":"K Kaplan","year":"2020","unstructured":"Kaplan K, Kaya Y, Kuncan M, Ertun\u00e7 HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses 139:109696. https:\/\/doi.org\/10.1016\/j.mehy.2020.109696","journal-title":"Med Hypotheses"},{"key":"3572_CR21","doi-asserted-by":"publisher","first-page":"101678","DOI":"10.1016\/j.bspc.2019.101678","volume":"57","author":"N Ghassemi","year":"2020","unstructured":"Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678. https:\/\/doi.org\/10.1016\/j.bspc.2019.101678","journal-title":"Biomed Signal Process Control"},{"issue":"6","key":"3572_CR22","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.3390\/app10061999","volume":"10","author":"MM Bad\u017ea","year":"2020","unstructured":"Bad\u017ea MM, Barjaktarovi\u0107 M\u010c (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6):1999. https:\/\/doi.org\/10.3390\/app10061999","journal-title":"Appl Sci"},{"issue":"2","key":"3572_CR23","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","volume":"39","author":"A Rehman","year":"2020","unstructured":"Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process 39(2):757\u2013775. https:\/\/doi.org\/10.1007\/s00034-019-01246-3","journal-title":"Circuits Syst Signal Process"},{"key":"3572_CR24","volume-title":"Artificial intelligence applications and innovations AIAI 2020 IFIP advances in information and communication technology","author":"R Chelghoum","year":"2020","unstructured":"Chelghoum R, Ikhlef A, Hameurlaine A, Jacquir S (2020) Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images. In: Maglogiannis I, Iliadis L, Pimenidis E (eds) Artificial intelligence applications and innovations AIAI 2020 IFIP advances in information and communication technology. Springer, Newyork"},{"issue":"3","key":"3572_CR25","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.13005\/bpj\/1991","volume":"13","author":"T Ruba","year":"2020","unstructured":"Ruba T, Tamilselvi R, Beham MP, Aparna N (2020) Accurate classification and detection of brain cancer cells in MRI and CT images using nano contrast agents. Biomed Pharmacol J 13(3):1227\u20131237. https:\/\/doi.org\/10.13005\/bpj\/1991","journal-title":"Biomed Pharmacol J"},{"key":"3572_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S et al (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116","journal-title":"Neurocomputing"},{"issue":"4","key":"3572_CR27","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"key":"3572_CR28","unstructured":"Zeiler MD (2012). ADADELTA: An adaptive learning rate method. CoRR. arXiv:1212.5701"},{"key":"3572_CR29","unstructured":"Goodfellow I, Bengio Y, Courville A (2015) Deep learning. MIT Press. http:\/\/www.deeplearningbook.org"},{"key":"3572_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556v6"},{"key":"3572_CR31","unstructured":"Kaiming H, Xiangyu Z, Shaoqing R, Jian S (2015) Deep residual learning for image recognition. arXiv:1512.03385"},{"key":"3572_CR32","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. arXiv:1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"issue":"8","key":"3572_CR33","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognit Lett"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03572-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-020-03572-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03572-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T10:47:18Z","timestamp":1624272438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-020-03572-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":33,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["3572"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03572-9","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"14 December 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is  no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Computer codes are publicly available on https:\/\/drive.google.com\/drive\/folders\/12elz8QU9SMUiiC7GBzSf9v2Hq-wBjhz4","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}