{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:24:03Z","timestamp":1774952643492,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s00521-022-07939-2","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T17:03:02Z","timestamp":1666458182000},"page":"4467-4478","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Brain MRI tumour classification using quantum classical convolutional neural net architecture"],"prefix":"10.1007","volume":"35","author":[{"given":"Rudrajit","family":"Choudhuri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6733-8135","authenticated-orcid":false,"given":"Amiya","family":"Halder","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"7939_CR1","doi-asserted-by":"crossref","unstructured":"Singh L, Chetty G, Sharma D (2012) A novel machine learning approach for detecting the brain abnormalities from mri structural images. In: IAPR international conference on pattern recognition in bioinformatics. Springer, pp 94\u2013105","DOI":"10.1007\/978-3-642-34123-6_9"},{"issue":"3","key":"7939_CR2","first-page":"53e6","volume":"4","author":"V Das","year":"2016","unstructured":"Das V, Rajan J (2016) Techniques for MRI brain tumor detection: a survey. Int J Res Comput Appl Inf Tech 4(3):53e6","journal-title":"Int J Res Comput Appl Inf Tech"},{"issue":"5","key":"7939_CR3","first-page":"8982","volume":"4","author":"KG Khambhata","year":"2016","unstructured":"Khambhata KG, Panchal SR (2016) Multiclass classification of brain tumor in MR images. Int J Innov Res Comput Commun Eng 4(5):8982","journal-title":"Int J Innov Res Comput Commun Eng"},{"key":"7939_CR4","doi-asserted-by":"crossref","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, pp 183\u2013189","DOI":"10.1007\/978-981-10-9035-6_33"},{"issue":"3","key":"7939_CR5","first-page":"760","volume":"6","author":"G Kaur","year":"2016","unstructured":"Kaur G and Rani J (2016) MRI brain tumor segmentation methods-a review. Int J Comput Eng Tech (IJCET) 6(3):760\u2013764","journal-title":"Int J Comput Eng Tech (IJCET)"},{"issue":"3","key":"7939_CR6","first-page":"1468","volume":"5","author":"A Kavitha","year":"2016","unstructured":"Kavitha A, Chitra L, Kanaga R (2016) Brain tumor segmentation using genetic algorithm with SVM classifier. Int J Adv Res Electr Electron Instrum Eng 5(3):1468","journal-title":"Int J Adv Res Electr Electron Instrum Eng"},{"issue":"4","key":"7939_CR7","doi-asserted-by":"publisher","first-page":"591","DOI":"10.7763\/IJCTE.2010.V2.207","volume":"2","author":"T Logeswari","year":"2010","unstructured":"Logeswari T, Karnan M (2010) An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. Int J Comput Theory Eng 2(4):591","journal-title":"Int J Comput Theory Eng"},{"key":"7939_CR8","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2009.06767","author":"D Konar","year":"2021","unstructured":"Konar D, Bhattacharyya S, Panigrahi BK, Behrman EC (2021) Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation. IEEE Trans Neural Netw Learn Syst. arXiv:2009.06767","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"7939_CR9","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","journal-title":"IEEE Trans Med Imaging"},{"key":"7939_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cmpb.2016.11.011","volume":"140","author":"IE Kaya","year":"2017","unstructured":"Kaya IE, Pehlivanl\u0131 A\u00c7, Sekizkarde\u015f EG, Ibrikci T (2017) PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Progr Biomed 140:19","journal-title":"Comput Methods Progr Biomed"},{"key":"7939_CR11","doi-asserted-by":"crossref","unstructured":"Mathew AR, Anto PB (2017) Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: 2017 International conference on signal processing and communication (ICSPC). IEEE, pp 75\u201378","DOI":"10.1109\/CSPC.2017.8305810"},{"key":"7939_CR12","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 JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60","journal-title":"Med Image Anal"},{"key":"7939_CR13","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G (2015) Recent advances in convolutional neural networks. arXiv:1512.07108"},{"key":"7939_CR14","doi-asserted-by":"crossref","unstructured":"Reza SM, Mays R, Iftekharuddin KM (2015) Multi-fractal detrended texture feature for brain tumor classification. In: Medical imaging 2015: computer-aided diagnosis, vol. 9414. International Society for Optics and Photonics, pp 9414:941410","DOI":"10.1117\/12.2083596"},{"key":"7939_CR15","doi-asserted-by":"crossref","unstructured":"Gupta T, Manocha P, Gandhi TK, Gupta R, Panigrahi BK (2017) Tumor classification and segmentation of MR brain images. arXiv:1710.11309","DOI":"10.1109\/SPIN.2018.8474246"},{"key":"7939_CR16","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.asoc.2015.09.016","volume":"38","author":"G Vishnuvarthanan","year":"2016","unstructured":"Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190","journal-title":"Appl Soft Comput"},{"issue":"6","key":"7939_CR17","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1002\/jemt.23238","volume":"82","author":"MA Khan","year":"2019","unstructured":"Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T, Zahoor S, Akram T (2019) Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82(6):909","journal-title":"Microsc Res Tech"},{"issue":"4","key":"7939_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4236\/jbise.2020.134004","volume":"13","author":"R Islam","year":"2020","unstructured":"Islam R, Imran S, Ashikuzzaman M, Khan MMA (2020) Detection and classification of brain tumor based on multilevel segmentation with convolutional neural network. J Biomed Sci Eng 13(4):45","journal-title":"J Biomed Sci Eng"},{"key":"7939_CR19","doi-asserted-by":"publisher","first-page":"105528","DOI":"10.1016\/j.asoc.2019.105528","volume":"82","author":"A Kumar","year":"2019","unstructured":"Kumar A, Ramachandran M, Gandomi AH, Patan R, Lukasik S, Soundarapandian RK (2019) A deep neural network based classifier for brain tumor diagnosis. Appl Soft Comput 82:105528","journal-title":"Appl Soft Comput"},{"issue":"15","key":"7939_CR20","doi-asserted-by":"publisher","first-page":"11107","DOI":"10.1007\/s11042-018-6901-9","volume":"79","author":"M Sharif","year":"2020","unstructured":"Sharif M, Amin J, Yasmin M, Rehman A (2020) Efficient hybrid approach to segment and classify exudates for DR prediction. Multimedia Tools Appl 79(15):11107","journal-title":"Multimedia Tools Appl"},{"issue":"6","key":"7939_CR21","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1038\/s41567-018-0124-x","volume":"14","author":"S Boixo","year":"2018","unstructured":"Boixo S, Isakov SV, Smelyanskiy VN, Babbush R, Ding N, Jiang Z, Bremner MJ, Martinis JM, Neven H (2018) Characterizing quantum supremacy in near-term devices. Nat Phys 14(6):595","journal-title":"Nat Phys"},{"issue":"7779","key":"7939_CR22","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1038\/s41586-019-1666-5","volume":"574","author":"F Arute","year":"2019","unstructured":"Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, Biswas R, Boixo S, Brandao FG, Buell DA et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505","journal-title":"Nature"},{"issue":"7","key":"7939_CR23","doi-asserted-by":"publisher","first-page":"074001","DOI":"10.1088\/1361-6633\/aab406","volume":"81","author":"V Dunjko","year":"2018","unstructured":"Dunjko V, Briegel HJ (2018) Machine learning and artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys 81(7):074001","journal-title":"Rep Prog Phys"},{"issue":"7671","key":"7939_CR24","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195","journal-title":"Nature"},{"issue":"2","key":"7939_CR25","doi-asserted-by":"publisher","first-page":"023023","DOI":"10.1088\/1367-2630\/18\/2\/023023","volume":"18","author":"JR McClean","year":"2016","unstructured":"McClean JR, Romero J, Babbush R, Aspuru-Guzik A (2016) The theory of variational hybrid quantum-classical algorithms. New J Phys 18(2):023023","journal-title":"New J Phys"},{"key":"7939_CR26","doi-asserted-by":"publisher","DOI":"10.36227\/techrxiv.12909860","author":"D Konar","year":"2021","unstructured":"Konar D, Bhattacharyya S, Gandhi TK, Panigrahi BK, Jiang R (2021) 3D Quantum-inspired self-supervised tensor network for volumetric segmentation of medical images. TechRxiv Prepr https:\/\/doi.org\/10.36227\/techrxiv.12909860","journal-title":"TechRxiv Prepr"},{"issue":"10","key":"7939_CR27","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, Burren Y, Porz N, Slotboom J, Wiest R et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"7939_CR28","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1136\/jnnp.74.3.288","volume":"74","author":"D Summers","year":"2003","unstructured":"Summers D (2003) Harvard whole brain atlas. J Neurol Neurosurg Psychiatry 74(3):288","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"13","key":"7939_CR29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.117.130501","volume":"117","author":"V Dunjko","year":"2016","unstructured":"Dunjko V, Taylor JM, Briegel HJ (2016) Quantum-enhanced machine learning. Phys Rev Lett 117(13):130501","journal-title":"Phys Rev Lett"},{"key":"7939_CR30","doi-asserted-by":"crossref","unstructured":"A\u00efmeur E, Brassard G, Gambs S (2006) Machine learning in a quantum world. In: Conference of the Canadian society for computational studies of intelligence. Springer, pp 431\u2013442","DOI":"10.1007\/11766247_37"},{"issue":"2","key":"7939_CR31","doi-asserted-by":"publisher","first-page":"022319","DOI":"10.1103\/PhysRevA.64.022319","volume":"64","author":"G Ortiz","year":"2001","unstructured":"Ortiz G, Gubernatis JE, Knill E, Laflamme R (2001) Quantum algorithms for fermionic simulations. Phys Rev A 64(2):022319","journal-title":"Phys Rev A"},{"issue":"15","key":"7939_CR32","doi-asserted-by":"publisher","first-page":"3393","DOI":"10.1002\/qua.21198","volume":"106","author":"AG Taube","year":"2006","unstructured":"Taube AG, Bartlett RJ (2006) New perspectives on unitary coupled-cluster theory. Int J Quantum Chem 106(15):3393","journal-title":"Int J Quantum Chem"},{"key":"7939_CR33","unstructured":"Bergholm V, Izaac J, Schuld M, Gogolin C, Alam MS, Ahmed S, Arrazola JM, Blank C, Delgado A, Jahangiri S, et\u00a0al. (2018) Pennylane: automatic differentiation of hybrid quantum-classical computations. arXiv:1811.04968"},{"issue":"1","key":"7939_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42484-020-00012-y","volume":"2","author":"M Henderson","year":"2020","unstructured":"Henderson M, Shakya S, Pradhan S, Cook T (2020) Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Mach Intell 2(1):1","journal-title":"Quantum Mach Intell"},{"key":"7939_CR35","unstructured":"Abohashima Z, Elhosen M, Houssein EH, Mohamed WM (2020) Classification with quantum machine learning: a survey. arXiv:2006.12270"},{"key":"7939_CR36","unstructured":"Broughton M, Verdon G, McCourt T, Martinez AJ, Yoo JH, Isakov SV, Massey P, Niu MY, Halavati R, Peters E, et\u00a0al (2020) Tensorflow quantum: a software framework for quantum machine learning. arXiv:2003.02989"},{"issue":"2","key":"7939_CR37","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s002110050165","volume":"72","author":"SL Campbell","year":"1995","unstructured":"Campbell SL, Gear CW (1995) The index of general nonlinear DAES. Numer Math 72(2):173","journal-title":"Numer Math"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07939-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07939-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07939-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T08:10:17Z","timestamp":1674979817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07939-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,22]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["7939"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07939-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,22]]},"assertion":[{"value":"14 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}