{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T15:19:18Z","timestamp":1771341558210,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"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":["Evol. Intel."],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s12065-020-00550-1","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T12:04:10Z","timestamp":1610625850000},"page":"1075-1087","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["MRI-based brain tumor detection using the fusion of histogram oriented gradients and neural features"],"prefix":"10.1007","volume":"14","author":[{"given":"Rafid","family":"Mostafiz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-2809","authenticated-orcid":false,"given":"Mohammad Shorif","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nur-A","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Mahmodul","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Motiur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"550_CR1","unstructured":"American Brain Tumor Association (2020) Available online: https:\/\/www.abta.org\/. Accessed 16 Jun 2020"},{"key":"550_CR2","doi-asserted-by":"crossref","unstructured":"Banerjee S, Mitra S, Masulli F, Rovetta S (2018) Brain tumor detection and classification from multi-sequence MRI: study using ConvNets. In: International MICCAI brainlesion workshop, pp 170\u2013179","DOI":"10.1007\/978-3-030-11723-8_17"},{"key":"550_CR3","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 (2020) Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recognit Lett 131:244\u2013260","journal-title":"Pattern Recognit Lett"},{"key":"550_CR4","doi-asserted-by":"crossref","unstructured":"Zhou C, Chen S, Ding C, Tao D (2018) Learning contextual and attentive information for brain tumor segmentation. In: International MICCAI brainlesion workshop, pp 497\u2013507","DOI":"10.1007\/978-3-030-11726-9_44"},{"issue":"1","key":"550_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/s00330-018-5595-8","volume":"29","author":"KR Laukamp","year":"2019","unstructured":"Laukamp KR et al (2019) Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol 29(1):124\u2013132. https:\/\/doi.org\/10.1007\/s00330-018-5595-8","journal-title":"Eur Radiol"},{"key":"550_CR6","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.neucom.2016.09.051","volume":"219","author":"S Abbasi","year":"2017","unstructured":"Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526\u2013535","journal-title":"Neurocomputing"},{"issue":"1","key":"550_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/cancers11010111","volume":"11","author":"GS Tandel","year":"2019","unstructured":"Tandel GS et al (2019) A review on a deep learning perspective in brain cancer classification. Cancers 11(1):111","journal-title":"Cancers"},{"key":"550_CR8","doi-asserted-by":"publisher","first-page":"21771","DOI":"10.1007\/s11042-020-08898-32020","volume":"79","author":"PK Chahal","year":"2020","unstructured":"Chahal PK, Pandey S, Goel S (2020) A survey on brain tumor detection techniques for MR images. Multimed Tools Appl 79:21771\u201321814. https:\/\/doi.org\/10.1007\/s11042-020-08898-32020","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"550_CR9","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1016\/j.mri.2013.05.002","volume":"31","author":"N Gordillo","year":"2013","unstructured":"Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426\u20131438","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"550_CR10","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 E-SA, El-Horbaty E-SM, Salem A-BM (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inform J 3(1):68\u201371","journal-title":"Future Comput Inform J"},{"issue":"3","key":"550_CR11","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.13005\/bpj\/1511","volume":"11","author":"J Seetha","year":"2018","unstructured":"Seetha J, Raja SS (2018) Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 11(3):1457","journal-title":"Biomed Pharmacol J"},{"issue":"1","key":"550_CR12","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.bspc.2006.05.002","volume":"1","author":"S Chaplot","year":"2006","unstructured":"Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86\u201392","journal-title":"Biomed Signal Process Control"},{"key":"550_CR13","unstructured":"Amin SE, Megeed MA (2012) Brain tumor diagnosis systems based on artificial neural networks and segmentation using MRI. In: 2012 8th international conference on informatics and systems (INFOS), p MM-119"},{"key":"550_CR14","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.cogsys.2019.09.007","volume":"59","author":"T Saba","year":"2020","unstructured":"Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221\u2013230","journal-title":"Cogn Syst Res"},{"key":"550_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2528\/PIER11031709","volume":"116","author":"YD Zhang","year":"2011","unstructured":"Zhang YD, Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Prog Electromagn Res 116:65\u201379","journal-title":"Prog Electromagn Res"},{"key":"550_CR16","first-page":"626","volume":"2","author":"K Bhagwat","year":"2013","unstructured":"Bhagwat K, More D, Shinde S, Daga A, Tornekar R (2013) Comparative study of brain tumor detection using K-means, fuzzy C means and hierarchical clustering algorithms. Int J Sci Eng Res 2:626\u2013632","journal-title":"Int J Sci Eng Res"},{"issue":"9","key":"550_CR17","first-page":"1192","volume":"17","author":"M Yasmin","year":"2012","unstructured":"Yasmin M, Sharif M, Masood S, Raza M, Mohsin S (2012) Brain image enhancement\u2014a survey. World Appl Sci J 17(9):1192\u20131204","journal-title":"World Appl Sci J"},{"key":"550_CR18","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.procs.2019.12.112","volume":"163","author":"N Salem","year":"2019","unstructured":"Salem N, Malik H, Shams A (2019) Medical image enhancement based on histogram algorithms. Proc Comput Sci 163:300\u2013311","journal-title":"Proc Comput Sci"},{"issue":"2","key":"550_CR19","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.ejrnm.2015.02.008","volume":"46","author":"S Madhukumar","year":"2015","unstructured":"Madhukumar S, Santhiyakumari N (2015) Evaluation of k-means and fuzzy C-means segmentation on MR images of brain. Egypt J Radiol Nucl Med 46(2):475\u2013479","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"2","key":"550_CR20","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3390\/bdcc3020027","volume":"3","author":"MS Alam","year":"2019","unstructured":"Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, Singh BC, Miah MS (2019) Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data Cogn Comput 3(2):27","journal-title":"Big Data Cogn Comput"},{"key":"550_CR21","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.procs.2015.08.057","volume":"58","author":"A Aslam","year":"2015","unstructured":"Aslam A, Khan E, Beg MS (2015) Improved edge detection algorithm for brain tumor segmentation. Proc Comput Sci 58:430\u2013437","journal-title":"Proc Comput Sci"},{"key":"550_CR22","doi-asserted-by":"crossref","unstructured":"Dam E, Loog M, Letteboer M (2004) Integrating automatic and interactive brain tumor segmentation. In: Proceedings of the 17th international conference on pattern recognition (ICPR 2004). IEEE, pp 790\u2013793","DOI":"10.1109\/ICPR.2004.1334647"},{"key":"550_CR23","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1148\/radiology.218.2.r01fe44586","volume":"218","author":"MR Kaus","year":"2001","unstructured":"Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R (2001) Automated segmentation of MR images of brain tumors. Radiology 218:586\u2013591","journal-title":"Radiology"},{"key":"550_CR24","first-page":"31","volume":"31","author":"R Meier","year":"2013","unstructured":"Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2013) A hybrid model for multimodal brain tumor segmentation. Multimodal Brain Tumor Segmentation 31:31\u201337","journal-title":"Multimodal Brain Tumor Segmentation"},{"issue":"10","key":"550_CR25","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"550_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.patcog.2018.05.006","volume":"82","author":"A Pinto","year":"2018","unstructured":"Pinto A, Pereira S, Rasteiro D, Silva CA (2018) Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognit 82:105\u2013117","journal-title":"Pattern Recognit"},{"key":"550_CR27","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol 1, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"550_CR28","doi-asserted-by":"crossref","unstructured":"Sarwinda D, Bustamam A (2018) 3D-HOG features-based classification using MRI images to early diagnosis of Alzheimer\u2019s disease. In: 2018 IEEE\/ACIS 17th international conference on computer and information science (ICIS), pp 457\u2013462","DOI":"10.1109\/ICIS.2018.8466524"},{"key":"550_CR29","unstructured":"Visual Geometry Group\u2014University of Oxford (2020) Available online: https:\/\/www.robots.ox.ac.uk\/~vgg\/research\/very_deep\/. Accessed 16 Jun 2020"},{"issue":"1","key":"550_CR30","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1504\/IJIM.2015.070024","volume":"1","author":"D Nandi","year":"2015","unstructured":"Nandi D, Ashour AS, Samanta S, Chakraborty S, Salem MA, Dey N (2015) Principal component analysis in medical image processing: a study. Int J Image Min 1(1):65\u201386","journal-title":"Int J Image Min"},{"issue":"8","key":"550_CR31","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1\u20132","key":"550_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1\u20132):1\u201339","journal-title":"Artif Intell Rev"},{"key":"550_CR33","doi-asserted-by":"publisher","unstructured":"El-Melegy MT, Abo El-Magd KM, Ali SA, Hussain KF, Mahdy YB (2019) Ensemble of multiple classifiers for automatic multimodal brain tumor segmentation. In: 2019 international conference on innovative trends in computer engineering (ITCE), Aswan, Egypt, pp 58\u201363. https:\/\/doi.org\/10.1109\/itce.2019.8646431","DOI":"10.1109\/itce.2019.8646431"},{"key":"550_CR34","doi-asserted-by":"crossref","unstructured":"Wang Z, Xiao H, He W, Wen F, Yuan K (2013) Real-time SIFT-based object recognition system. In: 2013 IEEE international conference on mechatronics and automation, pp 1361\u20131366","DOI":"10.1109\/ICMA.2013.6618111"},{"key":"550_CR35","doi-asserted-by":"crossref","unstructured":"Weninger L, Rippel O, Koppers S, Merhof D (2018) Segmentation of brain tumors and patient survival prediction: methods for the BRATS 2018 challenge. In: International MICCAI brainlesion workshop, pp 3\u201312","DOI":"10.1007\/978-3-030-11726-9_1"},{"key":"550_CR36","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.3906\/elk-1801-8","volume":"26","author":"A Ari","year":"2018","unstructured":"Ari A, Hanbay D (2018) Deep learning based brain tumor classification and detection system. Turk J Electr Eng Comput Sci 26:2275\u20132286","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"550_CR37","doi-asserted-by":"crossref","unstructured":"Suter Y, Jungo A, Rebsamen M, Knecht U, Herrmann E, Wiest R, Reyes M (2018) Deep learning versus classical regression for brain tumor patient survival prediction. In: International MICCAI brainlesion workshop. Springer, pp 429\u2013440","DOI":"10.1007\/978-3-030-11726-9_38"},{"key":"550_CR38","doi-asserted-by":"crossref","unstructured":"Li Y, Shen L (2017) Deep learning based multimodal brain tumor diagnosis. In: International MICCAI brainlesion workshop. Springer, pp 149\u2013158","DOI":"10.1007\/978-3-319-75238-9_13"},{"key":"550_CR39","doi-asserted-by":"crossref","unstructured":"Nie D, Zhang H, Adeli E, Liu L, Shen D (2016) 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 212\u2013220","DOI":"10.1007\/978-3-319-46723-8_25"},{"key":"550_CR40","doi-asserted-by":"crossref","unstructured":"Chato L, Latifi S (2017) Machine learning and deep learning techniques to predict overall survival of brain tumor patients using MRI images. In: International conference on bioinformatics and bioengineering (BIBE). IEEE, pp 9\u201314","DOI":"10.1109\/BIBE.2017.00-86"},{"key":"550_CR41","doi-asserted-by":"publisher","first-page":"3571","DOI":"10.1007\/s11042-018-6176-1","volume":"79","author":"B Amarapur","year":"2020","unstructured":"Amarapur B (2020) Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier. Multimed Tools Appl 79:3571\u20133599","journal-title":"Multimed Tools Appl"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-020-00550-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-020-00550-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-020-00550-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T06:35:20Z","timestamp":1622442920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-020-00550-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,14]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["550"],"URL":"https:\/\/doi.org\/10.1007\/s12065-020-00550-1","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,14]]},"assertion":[{"value":"27 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2021","order":4,"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 the research was conducted in the absence of any commercial or financial relationships. Therefore, there are no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with animals and human performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}}]}}