{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:10:05Z","timestamp":1775254205519,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"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":[[2022,11]]},"DOI":"10.1007\/s00521-022-07583-w","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T05:02:37Z","timestamp":1658206957000},"page":"20191-20201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel automatic approach for glioma segmentation"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8516-763X","authenticated-orcid":false,"given":"Wajdi","family":"Elhamzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wadhah","family":"Ayadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Atri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"issue":"2","key":"7583_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s12021-014-9245-2","volume":"13","author":"NJ Tustison","year":"2015","unstructured":"Tustison NJ, Shrinidhi KL, Wintermark M, Durst CR, Kandel BM, Gee JC, Avants BB (2015) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2):209\u2013225","journal-title":"Neuroinformatics"},{"issue":"5","key":"7583_CR2","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA (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":"7583_CR3","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.patrec.2017.05.028","volume":"94","author":"V Rajinikanth","year":"2017","unstructured":"Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images\u2013a study with teaching learning based optimization. Pattern Recogn Lett 94:87\u201395","journal-title":"Pattern Recogn Lett"},{"key":"7583_CR4","doi-asserted-by":"crossref","unstructured":"Rajinikanth V, Fernandes SL, Bhushan B, Sunder NR (2018) Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. In: Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. Springer, Singapore, pp 313\u2013321","DOI":"10.1007\/978-981-10-4280-5_33"},{"issue":"9","key":"7583_CR5","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/s10916-019-1428-9","volume":"43","author":"UR Acharya","year":"2019","unstructured":"Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, Yeong CH (2019) Automated detection of Alzheimer\u2019s disease using brain MRI images\u2013a study with various feature extraction techniques. J Med Syst 43(9):302","journal-title":"J Med Syst"},{"issue":"2","key":"7583_CR6","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TRPMS.2018.2890359","volume":"3","author":"Z Guo","year":"2019","unstructured":"Guo Z, Li X, Huang H, Guo N, Li Q (2019) Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci 3(2):162\u2013169","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"issue":"10","key":"7583_CR7","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Lanczi L (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imag 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imag"},{"issue":"6","key":"7583_CR8","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310\u20131320","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"7583_CR9","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/42.836373","volume":"19","author":"LG Ny\u00fal","year":"2000","unstructured":"Ny\u00fal LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19(2):143\u2013150","journal-title":"IEEE Trans Med Imaging"},{"key":"7583_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"7583_CR11","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11063-020-10398-2","volume":"53","author":"W Ayadi","year":"2021","unstructured":"Ayadi W, Elhamzi W, Charfi I et al (2021) Deep CNN for brain tumor classification. Neural Process Lett 53:671\u2013700. https:\/\/doi.org\/10.1007\/s11063-020-10398-2","journal-title":"Neural Process Lett"},{"key":"7583_CR12","unstructured":"Kalinovsky A, Kovalev V (2016) Lung image Ssgmentation using deep learning methods and convolutional neural networks. In: XIII international conference on pattern recognition and information processing (PRIP), Minsk, Belarus"},{"issue":"4","key":"7583_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12918-018-0572-z","volume":"12","author":"M Fu","year":"2018","unstructured":"Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, Gong X (2018) Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol 12(4):56","journal-title":"BMC Syst Biol"},{"key":"7583_CR14","doi-asserted-by":"crossref","unstructured":"Roth HR., Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 556\u2013564","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"7583_CR15","unstructured":"Y, L, Yang X, Chen H, Qin J, Heng PA (2017) Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 36\u201372"},{"issue":"10","key":"7583_CR16","doi-asserted-by":"publisher","first-page":"5221","DOI":"10.1002\/mp.12480","volume":"44","author":"X Zhou","year":"2017","unstructured":"Zhou X, Takayama R, Wang S, Hara T, Fujita H (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10):5221\u20135233","journal-title":"Med Phys"},{"key":"7583_CR17","doi-asserted-by":"crossref","unstructured":"Trullo R, Petitjean C, Nie D, Shen D, Ruan S (2017) Joint segmentation of multiple thoracic organs in CT images with two collaborative deep architectures. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp 21\u201329","DOI":"10.1007\/978-3-319-67558-9_3"},{"key":"7583_CR18","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 3:100004","journal-title":"Array"},{"key":"7583_CR19","doi-asserted-by":"crossref","unstructured":"Wang G, Li W, Ourselin S, Vercauteren T (2017) Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International MICCAI brainlesion workshop, pp 178\u2013190","DOI":"10.1007\/978-3-319-75238-9_16"},{"key":"7583_CR20","doi-asserted-by":"crossref","unstructured":"Zhou C, Ding C, Lu Z, Wang X, Tao D (2018) One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 637\u2013645","DOI":"10.1007\/978-3-030-00931-1_73"},{"key":"7583_CR21","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.cmpb.2018.09.007","volume":"166","author":"R Saouli","year":"2018","unstructured":"Saouli R, Akil M, Kachouri R (2018) Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput Methods Programs Biomed 166:39\u201349","journal-title":"Comput Methods Programs Biomed"},{"key":"7583_CR22","doi-asserted-by":"crossref","unstructured":"Rezaei M, Harmuth K, Gierke W, Kellermeier T, Fischer M, Yang H, Meinel C (2017) A conditional adversarial network for semantic segmentation of brain tumor. In\u00a0International MICCAI Brainlesion Workshop, pp 241\u2013252","DOI":"10.1007\/978-3-319-75238-9_21"},{"key":"7583_CR23","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.compbiomed.2019.03.014","volume":"108","author":"H Li","year":"2019","unstructured":"Li H, Li A, Wang M (2019) A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Comput Biol Med 108:150\u2013160","journal-title":"Comput Biol Med"},{"key":"7583_CR24","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1007\/s11760-020-01699-z","volume":"14","author":"Z Barzegar","year":"2020","unstructured":"Barzegar Z, Jamzad M (2020) A reliable ensemble-based classification framework for glioma brain tumor segmentation. SIViP 14:1591\u20131599","journal-title":"SIViP"},{"key":"7583_CR25","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, Huang W, Gao X (2019) Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access 7:92615\u201392629","journal-title":"IEEE Access"},{"key":"7583_CR26","doi-asserted-by":"publisher","first-page":"101406","DOI":"10.1109\/ACCESS.2020.2998601","volume":"8","author":"NM Aboelenein","year":"2020","unstructured":"Aboelenein NM, Songhao P, Koubaa A, Noor A, Afifi A (2020) HTTU-net: hybrid two track U-net for automatic brain tumor segmentation. IEEE Access 8:101406\u2013101415","journal-title":"IEEE Access"},{"key":"7583_CR27","doi-asserted-by":"crossref","unstructured":"Ghaffari M, Sowmya A, Oliver R (2020) Brain tumour segmentation using cascaded 3D densely-connected U-net. arXiv, eess, arXiv:2009.07563","DOI":"10.1007\/978-3-030-72084-1_43"},{"key":"7583_CR28","doi-asserted-by":"crossref","unstructured":"Nguyen HT, Le TT, Nguyen TV, Nguyen NT (2020) Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network. arXiv, eess arXiv:2009.12111","DOI":"10.1007\/978-3-030-72084-1_45"},{"key":"7583_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06010-w","author":"PK Chahal","year":"2021","unstructured":"Chahal PK, Pandey S (2021) A hybrid weighted fuzzy approach for brain tumor segmentation using MR images. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06010-w","journal-title":"Neural Comput Appl"},{"key":"7583_CR30","unstructured":"O'Shea K, Nash R (2015) An introduction to convolutional neural networks.\u00a0arXiv, cs, arXiv:1511.08458"},{"key":"7583_CR31","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.jvcir.2018.11.047","volume":"58","author":"J Chang","year":"2019","unstructured":"Chang J, Zhang L, Gu N, Zhang X, Ye M, Yin R, Meng Q (2019) A mix-pooling CNN architecture with FCRF for brain tumor segmentation. J Vis Commun Image Represent 58:316\u2013322","journal-title":"J Vis Commun Image Represent"},{"key":"7583_CR32","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift.\u00a0arXiv, cs, arXiv:1502.03167"},{"key":"7583_CR33","unstructured":"Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th international conference on machine learning, 3, Atlanta, Georgia, USA, vol 30, no 1"},{"issue":"7553","key":"7583_CR34","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"1","key":"7583_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"7583_CR36","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.artmed.2018.08.008","volume":"95","author":"J Bernal","year":"2019","unstructured":"Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Mart\u00ed R, Llad\u00f3 X (2019) Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 95:64\u201381","journal-title":"Artif Intell Med"},{"key":"7583_CR37","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patcog.2018.11.009","volume":"88","author":"S Chen","year":"2019","unstructured":"Chen S, Ding C, Liu M (2019) Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn 88:90\u2013100","journal-title":"Pattern Recogn"},{"key":"7583_CR38","doi-asserted-by":"crossref","unstructured":"Dvo\u0159\u00e1k P, Menze B (2015) Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. In: International MICCAI workshop on medical computer vision, pp 59\u201371","DOI":"10.1007\/978-3-319-42016-5_6"},{"key":"7583_CR39","first-page":"1","volume":"2","author":"HS Nogay","year":"2021","unstructured":"Nogay HS, Akinci TC, Yilmaz M et al (2021) Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network. Neural Comput Appl 2:1\u201310","journal-title":"Neural Comput Appl"},{"key":"7583_CR40","first-page":"1","volume":"5","author":"A Revathi","year":"2022","unstructured":"Revathi A, Sasikaladevi N, Arunprasanth D et al (2022) Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models. Neural Comput Appl 5:1\u201318","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07583-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07583-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07583-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T20:56:09Z","timestamp":1666299369000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07583-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,19]]},"references-count":40,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7583"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07583-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,19]]},"assertion":[{"value":"4 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}