{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T16:39:31Z","timestamp":1773592771371,"version":"3.50.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T00:00:00Z","timestamp":1600387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T00:00:00Z","timestamp":1600387200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05332-5","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T18:02:41Z","timestamp":1600452161000},"page":"5543-5553","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Multiscale-based multimodal image classification of brain tumor using deep learning method"],"prefix":"10.1007","volume":"33","author":[{"given":"R.","family":"Rajasree","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. Christopher","family":"Columbus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Shilaja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"issue":"6","key":"5332_CR1","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/TST.2014.6961028","volume":"19","author":"L Jin","year":"2014","unstructured":"Jin L et al (2014) A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol 19(6):578\u2013595","journal-title":"Tsinghua Sci Technol"},{"issue":"suppl 2","key":"5332_CR2","first-page":"31","volume":"75","author":"IR Whittle","year":"2004","unstructured":"Whittle IR (2004) The dilemma of low grade glioma. J Neurol Neurosurg Psychiatry 75(suppl 2):31","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"6","key":"5332_CR3","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/TMI.2018.2823756","volume":"37","author":"E Kang","year":"2018","unstructured":"Kang E et al (2018) Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans Med Imaging 37(6):1358\u20131369","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"5332_CR4","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5332_CR5","doi-asserted-by":"crossref","unstructured":"Fan-Hui, K (2012) Image retrieval based on Gaussian Mixture Model. In: 2012 international conference on machine learning and cybernetics","DOI":"10.1109\/ICMLC.2012.6359498"},{"issue":"4","key":"5332_CR6","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/TNNLS.2012.2235460","volume":"24","author":"R Savitha","year":"2013","unstructured":"Savitha R, Suresh S, Sundararajan N (2013) Projection-based fast learning fully complex-valued relaxation neural network. IEEE Trans Neural Netw Learn Syst 24(4):529\u2013541","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"5332_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/42.906424","volume":"20","author":"Y Zhang","year":"2001","unstructured":"Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation\u2013maximization algorithm. IEEE Trans Med Imaging 20(1):45\u201357","journal-title":"IEEE Trans Med Imaging"},{"key":"5332_CR8","doi-asserted-by":"crossref","unstructured":"Shahzadi I, Tang TB, Meriadeau F, Quyyum A (2018) CNN-LSTM: cascaded framework for brain tumour classification. In: 2018 IEEE-EMBS conference on biomedical engineering and sciences (IECBES), Dec 2018. IEEE, pp 633\u2013637","DOI":"10.1109\/IECBES.2018.8626704"},{"issue":"4","key":"5332_CR9","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus Z et al (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449\u2013459","journal-title":"J Digit Imaging"},{"issue":"7","key":"5332_CR10","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/TMI.2012.2230403","volume":"32","author":"WK Moon","year":"2013","unstructured":"Moon WK et al (2013) Computer-aided tumor detection based on Multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Trans Med Imaging 32(7):1191\u20131200","journal-title":"IEEE Trans Med Imaging"},{"key":"5332_CR11","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1590\/1517-3151.0517","volume":"30","author":"VM Gon\u00e7alves","year":"2014","unstructured":"Gon\u00e7alves VM, Delamaro ME, Nunes FdLdS (2014) A systematic review on the evaluation and characteristics of computer-aided diagnosis systems. Revista Brasileira de Engenharia Biom\u00e9dica 30:355\u2013383","journal-title":"Revista Brasileira de Engenharia Biom\u00e9dica"},{"issue":"5","key":"5332_CR12","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","volume":"35","author":"T Brosch","year":"2016","unstructured":"Brosch T et al (2016) Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans Med Imaging 35(5):1229\u20131239","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"5332_CR13","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.3174\/ajnr.A5543","volume":"39","author":"G Zaharchuk","year":"2018","unstructured":"Zaharchuk G et al (2018) Deep learning in neuroradiology. Am J Neuroradiol 39(10):1776\u20131784","journal-title":"Am J Neuroradiol"},{"issue":"5","key":"5332_CR14","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S et al (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240\u20131251","journal-title":"IEEE Trans Med Imaging"},{"key":"5332_CR15","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1155\/2016\/8356294","volume":"2016","author":"L Zhao","year":"2016","unstructured":"Zhao L, Jia K (2016) Multiscale CNNs for brain tumor segmentation and diagnosis. Comput Math Methods Med 2016:7","journal-title":"Comput Math Methods Med"},{"issue":"2","key":"5332_CR16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s11548-016-1483-3","volume":"12","author":"M Soltaninejad","year":"2017","unstructured":"Soltaninejad M et al (2017) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 12(2):183\u2013203","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5332_CR17","doi-asserted-by":"crossref","unstructured":"Dong H et al (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Medical image understanding and analysis 2017. pp 506\u2013517","DOI":"10.1007\/978-3-319-60964-5_44"},{"key":"5332_CR18","doi-asserted-by":"crossref","unstructured":"Hu Y, Xia Y (2018) 3D Deep neural network-based brain tumor segmentation using multimodality magnetic resonance sequences. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, Cham","DOI":"10.1007\/978-3-319-75238-9_36"},{"issue":"4","key":"5332_CR19","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1109\/LSP.2016.2611485","volume":"24","author":"X Wang","year":"2017","unstructured":"Wang X et al (2017) Beyond frame-level CNN: saliency-aware 3-D CNN With LSTM for video action recognition. IEEE Signal Process Lett 24(4):510\u2013514","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"5332_CR20","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1049\/iet-cvi.2015.0473","volume":"11","author":"S Li","year":"2017","unstructured":"Li S et al (2017) Generating image descriptions with multidirectional 2D long short-term memory. IET Comput Vision 11(1):104\u2013111","journal-title":"IET Comput Vision"},{"key":"5332_CR21","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neucom.2016.12.088","volume":"268","author":"E Tsironi","year":"2017","unstructured":"Tsironi E et al (2017) An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition. Neurocomputing 268:76\u201386","journal-title":"Neurocomputing"},{"issue":"13","key":"5332_CR22","doi-asserted-by":"publisher","first-page":"0031","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"Bauer S et al (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):0031\u20139155","journal-title":"Phys Med Biol"},{"issue":"10","key":"5332_CR23","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05332-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05332-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05332-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T00:32:48Z","timestamp":1631925168000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05332-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,18]]},"references-count":23,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5332"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05332-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,18]]},"assertion":[{"value":"13 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2020","order":3,"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 declared that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}