{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:06:06Z","timestamp":1783944366128,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11042-022-13545-0","type":"journal-article","created":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T02:02:32Z","timestamp":1659146552000},"page":"37541-37567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification"],"prefix":"10.1007","volume":"81","author":[{"given":"Aman","family":"Verma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6823-2524","authenticated-orcid":false,"given":"Vibhav Prakash","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"13545_CR1","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-981-10-9035-6_33","volume-title":"World congress on medical physics and biomedical engineering 2018","author":"N Abiwinanda","year":"2019","unstructured":"Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2019) Brain tumor classification using convolutional neural network. In: World congress on medical physics and biomedical engineering 2018. Springer, Singapore, pp 183\u2013189"},{"key":"13545_CR2","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1109\/ICASSP.2019.8683759","volume-title":"ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP)","author":"P Afshar","year":"2019","unstructured":"Afshar P, Plataniotis KN, Mohammadi A (2019) Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1368\u20131372"},{"issue":"3","key":"13545_CR3","doi-asserted-by":"publisher","first-page":"587","DOI":"10.2214\/ajr.155.3.2167004","volume":"155","author":"PC Buetow","year":"1990","unstructured":"Buetow PC, Smirniotopoulos JG, Done S (1990) Congenital brain tumors: a review of 45 cases. AJR Am J Roentgenol 155(3):587\u2013593","journal-title":"AJR Am J Roentgenol"},{"issue":"8","key":"13545_CR4","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.3390\/app9081618","volume":"9","author":"D Cascio","year":"2019","unstructured":"Cascio D, Taormina V, Raso G (2019) Deep CNN for IIF images classification in autoimmune diagnostics. Appl Sci 9(8):1618","journal-title":"Appl Sci"},{"key":"13545_CR5","doi-asserted-by":"publisher","unstructured":"Cheng J (2017) Brain tumor dataset (version 5). Figshare. Retrieved 16 November 2020 from https:\/\/doi.org\/10.6084\/m9.figshare.1512427.v5","DOI":"10.6084\/m9.figshare.1512427.v5"},{"issue":"10","key":"13545_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140381","volume":"10","author":"J Cheng","year":"2015","unstructured":"Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10):e0140381","journal-title":"PLoS ONE"},{"key":"13545_CR7","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280\u2013296","journal-title":"Med Image Anal"},{"key":"13545_CR8","first-page":"1251","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"F Chollet","year":"2017","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258"},{"key":"13545_CR9","doi-asserted-by":"crossref","unstructured":"Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Computerized Medical Imaging and Graphics 111:103345","DOI":"10.1016\/j.compbiomed.2019.103345"},{"key":"13545_CR10","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","volume-title":"2009 IEEE conference on computer vision and pattern recognition","author":"J Deng","year":"2009","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255"},{"issue":"4\u20135","key":"13545_CR11","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society 31(4\u20135):198\u2013211. https:\/\/doi.org\/10.1016\/j.compmedimag.2007.02.002","journal-title":"Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society"},{"key":"13545_CR12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/BHI.2017.7897215","volume-title":"2017 IEEE EMBS international conference on biomedical & health informatics (BHI)","author":"Y Dong","year":"2017","unstructured":"Dong Y, Jiang Z, Shen H, Pan WD, Williams LA, Reddy VV, Benjamin W, Bryan AW (2017) Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. In: 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE, pp 101\u2013104"},{"issue":"2","key":"13545_CR13","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37(2):505\u2013515","journal-title":"Radiographics"},{"key":"13545_CR14","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","journal-title":"Biomed Signal Process Control"},{"key":"13545_CR15","first-page":"249","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics","author":"X Glorot","year":"2010","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249\u2013256"},{"key":"13545_CR16","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","journal-title":"IEEE Access"},{"key":"13545_CR17","unstructured":"Harvard Medical School, http:\/\/med.harvard.edu\/AANLIB\/"},{"key":"13545_CR18","first-page":"630","volume-title":"European conference on computer vision","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, pp 630\u2013645"},{"issue":"02","key":"13545_CR19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowledge-Based Syst 6(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowledge-Based Syst"},{"key":"13545_CR20","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"13545_CR21","first-page":"4700","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708"},{"key":"13545_CR22","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. PMLR 448\u2013456"},{"key":"13545_CR23","doi-asserted-by":"crossref","first-page":"0252","DOI":"10.1109\/EIT.2018.8500308","volume-title":"2018 IEEE international conference on electro\/information technology (EIT)","author":"MR Ismael","year":"2018","unstructured":"Ismael MR, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE international conference on electro\/information technology (EIT). IEEE, pp 0252\u20130257"},{"issue":"3","key":"13545_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01069-2","volume":"31","author":"T Kaur","year":"2020","unstructured":"Kaur T, Gandhi TK (2020) Deep convolutional neural networks with transfer learning for automated brain image classification. Mach Vis Appl 31(3):1\u201316","journal-title":"Mach Vis Appl"},{"key":"13545_CR25","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"13545_CR26","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Proces Syst"},{"issue":"7553","key":"13545_CR27","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"},{"key":"13545_CR28","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"13545_CR29","first-page":"100003","volume":"2","author":"R Mehrotra","year":"2020","unstructured":"Mehrotra R, Ansari MA, Agrawal R, Anand RS (2020) A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl 2:100003","journal-title":"Mach Learn Appl"},{"key":"13545_CR30","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML"},{"key":"13545_CR31","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2015.11.034","volume":"177","author":"DR Nayak","year":"2016","unstructured":"Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188\u2013197","journal-title":"Neurocomputing"},{"key":"13545_CR32","first-page":"3347","volume-title":"Advances in neural information processing systems","author":"M Raghu","year":"2019","unstructured":"Raghu M, Zhang C, Kleinberg J, Bengio S (2019) Transfusion: understanding transfer learning for medical imaging. In: Advances in neural information processing systems, pp 3347\u20133357"},{"key":"13545_CR33","doi-asserted-by":"crossref","unstructured":"Ranjan A, Singh VP, Mishra RB, Thakur AK, Singh AK (2021) Sentence polarity detection using stepwise greedy correlation based feature selection and random forests: an fMRI study. Journal of Neurolinguistics 59:100985","DOI":"10.1016\/j.jneuroling.2021.100985"},{"issue":"5","key":"13545_CR34","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285\u20131298","journal-title":"IEEE Trans Med Imaging"},{"key":"13545_CR35","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"1","key":"13545_CR36","first-page":"14","volume":"1","author":"TS Surawicz","year":"1999","unstructured":"Surawicz TS, McCarthy BJ, Kupelian V, Jukich PJ, Bruner JM, Davis FG (1999) Descriptive epidemiology of primary brain and CNS tumors: results from the central brain tumor registry of the United States, 1990-1994. Neuro-oncology 1(1):14\u201325","journal-title":"Neuro-oncology"},{"key":"13545_CR37","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZNK Swati","year":"2019","unstructured":"Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34\u201346","journal-title":"Comput Med Imaging Graph"},{"key":"13545_CR38","first-page":"1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C Szegedy","year":"2015","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139"},{"key":"13545_CR39","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"13545_CR40","first-page":"2818","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C Szegedy","year":"2016","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826"},{"key":"13545_CR41","doi-asserted-by":"crossref","unstructured":"Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, \u2026 Wong TY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama 318(22):2211\u20132223","DOI":"10.1001\/jama.2017.18152"},{"key":"13545_CR42","doi-asserted-by":"publisher","first-page":"109531","DOI":"10.1016\/j.mehy.2019.109531","volume":"134","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z (2020) BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 134:109531","journal-title":"Med Hypotheses"},{"key":"13545_CR43","doi-asserted-by":"publisher","first-page":"107138","DOI":"10.1016\/j.comnet.2020.107138","volume":"171","author":"D Vasan","year":"2020","unstructured":"Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) IMCFN: image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138","journal-title":"Comput Netw"},{"key":"13545_CR44","first-page":"5998","volume-title":"Advances in neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"issue":"4","key":"13545_CR45","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/MSP.2010.936730","volume":"27","author":"MN Wernick","year":"2010","unstructured":"Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC (2010) Machine learning in medical imaging. IEEE Signal Process Mag 27(4):25\u201338","journal-title":"IEEE Signal Process Mag"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13545-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13545-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13545-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T06:37:21Z","timestamp":1727678241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13545-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":45,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["13545"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13545-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,30]]},"assertion":[{"value":"10 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interest"}},{"value":"This article does not contain any studies with human participants or animals by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}