{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:02:39Z","timestamp":1780401759275,"version":"3.54.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007950","name":"Tanta University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007950","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Timely prognosis of brain tumors has a crucial role for powerful healthcare of remedy-making plans. Manual classification of the brain tumors in magnetic resonance imaging (MRI) images is a challenging task, which relies on the experienced radiologists to identify and classify the brain tumor. Automated classification of different brain tumors is significant based on designing computer-aided diagnosis (CAD) systems. Existing classification methods suffer from unsatisfactory performance and\/or large computational cost\/ time. This paper proposed a fast and efficient classification process, called BTC-fCNN, which is a deep learning-based system to distinguish between different views of three brain tumor types, namely meningioma, glioma, and pituitary tumors. The proposed system\u2019s model was applied on MRI images from the Figshare dataset. It consists of 13 layers with few trainable parameters involving convolution layer, 1\u2009\u00d7\u20091 convolution layer, average pooling, fully connected layer, and softmax layer. Five iterations including transfer learning and five-fold cross-validation for retraining are considered to increase the proposed model performance. The proposed model achieved 98.63% average accuracy, using five iterations with transfer learning, and 98.86% using retrained five-fold cross-validation (internal transfer learning between the folds). Various evaluation metrics were measured to evaluate the proposed model, such as precision, F-score, recall, specificity and confusion matrix. The proposed BTC-fCNN model outstrips the state-of-the-art and other well-known convolution neural networks (CNN).<\/jats:p>","DOI":"10.1007\/s13755-022-00203-w","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T05:02:17Z","timestamp":1672635737000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification"],"prefix":"10.1007","volume":"11","author":[{"given":"Basant S.","family":"Abd El-Wahab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed E.","family":"Nasr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salah","family":"Khamis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amira S.","family":"Ashour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"203_CR1","doi-asserted-by":"publisher","first-page":"75822","DOI":"10.1109\/ACCESS.2020.2989143","volume":"8","author":"T Zhang","year":"2020","unstructured":"Zhang T, Sodhro AH, Luo Z, Zahid N, Nawaz MW, Pirbhulal S, Muzammal M. A joint deep learning and internet of medical things driven framework for elderly patients. IEEE Access. 2020;8:75822\u201332.","journal-title":"IEEE Access"},{"issue":"1","key":"203_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3374760","volume":"16","author":"H Zhang","year":"2020","unstructured":"Zhang H, Zhang H, Pirbhulal S, Wu W, Albuquerque VHCD. Active balancing mechanism for imbalanced medical data in deep learning\u2013based classification models. ACM Trans Multimedia Comput Commun Appl. 2020;16(1):1\u201315.","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"203_CR3","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.inffus.2019.06.021","volume":"53","author":"M Muzammal","year":"2020","unstructured":"Muzammal M, Talat R, Sodhro AH, Pirbhulal S. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Inf Fusion. 2020;53:155\u201364.","journal-title":"Inf Fusion"},{"key":"203_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2021.166273","volume":"230","author":"UK Acharya","year":"2021","unstructured":"Acharya UK, Kumar S. Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement. Optik. 2021;230: 166273.","journal-title":"Optik"},{"issue":"1","key":"203_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s12204-021-2264-x","volume":"26","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Liu S, Li C, Wang J. Rethinking the dice loss for deep learning lesion segmentation in medical images. J Shanghai Jiaotong Univ (Science). 2021;26(1):93\u2013102.","journal-title":"J Shanghai Jiaotong Univ (Science)"},{"issue":"1","key":"203_CR6","first-page":"012010","volume":"1801","author":"AS Miroshnichenko","year":"2021","unstructured":"Miroshnichenko AS, Mikhelev VM. Classification of medical images of patients with Covid-19 using transfer learning technology of convolutional neural network. J Phy: Conf Series. 2021;1801(1):012010.","journal-title":"J Phy: Conf Series"},{"issue":"10","key":"203_CR7","doi-asserted-by":"publisher","first-page":"e0140381","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, Feng Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE. 2015;10(10):e0140381.","journal-title":"PLoS ONE"},{"key":"203_CR8","doi-asserted-by":"crossref","unstructured":"Ismael MR, Abdel-Qader I. Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE international conference on electro\/information technology (EIT), 2018. p. 0252\u20130257","DOI":"10.1109\/EIT.2018.8500308"},{"issue":"5","key":"203_CR9","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. Deep learning based brain tumor classification and detection system. Turk J Electr Eng Comput Sci. 2018;26(5):2275\u201386.","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"203_CR10","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. A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access. 2019;7:36266\u201373.","journal-title":"IEEE Access"},{"key":"203_CR11","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","volume":"30","author":"M Sajjad","year":"2019","unstructured":"Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci. 2019;30:174\u201382.","journal-title":"J Comput Sci"},{"issue":"9","key":"203_CR12","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.3390\/s19091992","volume":"19","author":"H Kutlu","year":"2019","unstructured":"Kutlu H, Avc\u0131 E. A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors. 2019;19(9):1992.","journal-title":"Sensors"},{"key":"203_CR13","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. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34\u201346.","journal-title":"Comput Med Imaging Graph"},{"key":"203_CR14","doi-asserted-by":"crossref","unstructured":"Ghosal P, Nandanwar L, Kanchan S, Bhadra A, Chakraborty J, Nandi D. Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019 p. 1\u20136, IEEE.","DOI":"10.1109\/ICACCP.2019.8882973"},{"issue":"1","key":"203_CR15","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bbe.2018.10.004","volume":"39","author":"AK Anaraki","year":"2019","unstructured":"Anaraki AK, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics Biomed Eng. 2019;39(1):63\u201374.","journal-title":"Biocybernetics Biomed Eng"},{"key":"203_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 2019;111: 103345.","journal-title":"Comput Biol Med"},{"issue":"19","key":"203_CR17","doi-asserted-by":"publisher","first-page":"28897","DOI":"10.1007\/s11042-021-10927-8","volume":"80","author":"M Alshayeji","year":"2021","unstructured":"Alshayeji M, Al-Buloushi J, Ashkanani A, Abed SE. Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture. Multimed Tools Appl. 2021;80(19):28897\u2013917.","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"203_CR18","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1002\/ima.22554","volume":"31","author":"J Kakarla","year":"2021","unstructured":"Kakarla J, Isunuri BV, Doppalapudi KS, Bylapudi KSR. Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network. Int J Imaging Syst Technol. 2021;31(3):1731\u201340.","journal-title":"Int J Imaging Syst Technol"},{"issue":"9","key":"203_CR19","doi-asserted-by":"publisher","first-page":"13429","DOI":"10.1007\/s11042-020-10335-4","volume":"80","author":"RL Kumar","year":"2021","unstructured":"Kumar RL, Kakarla J, Isunuri BV, Singh M. Multi-class brain tumor classification using residual network and global average pooling. Multimed Tools Appl. 2021;80(9):13429\u201338.","journal-title":"Multimed Tools Appl"},{"key":"203_CR20","unstructured":"Lin M, Chen Q, Yan S. Network in network. 2013 arXiv preprint https:\/\/arXiv.org\/1312.4400"},{"key":"203_CR21","doi-asserted-by":"publisher","unstructured":"Cheng J. Brain tumor dataset. Figshare. Dataset. 2017.  https:\/\/doi.org\/10.6084\/m9.figshare.1512427.v5, https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427. Accessed 1 May 2022.","DOI":"10.6084\/m9.figshare.1512427.v5"},{"key":"203_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2021.110798","volume":"315","author":"B Botta","year":"2022","unstructured":"Botta B, Gattam SSR, Datta AK. Eggshell crack detection using deep convolutional neural networks. J Food Eng. 2022;315: 110798.","journal-title":"J Food Eng"},{"key":"203_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102920","volume":"69","author":"S Thakur","year":"2021","unstructured":"Thakur S, Kumar A. X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN). Biomed Signal Process Control. 2021;69: 102920.","journal-title":"Biomed Signal Process Control"},{"key":"203_CR24","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.patcog.2017.05.012","volume":"70","author":"LG Hafemann","year":"2017","unstructured":"Hafemann LG, Sabourin R, Oliveira LS. Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 2017;70:163\u201376.","journal-title":"Pattern Recogn"},{"key":"203_CR25","first-page":"23","volume":"5","author":"J Wu","year":"2017","unstructured":"Wu J. Introduction to convolutional neural networks, national key lab for novel software technology. Nanjing Univ China. 2017;5:23.","journal-title":"Nanjing Univ China"},{"key":"203_CR26","unstructured":"Liu W, Wen Y, Yu Z, Yang M. Large-margin softmax loss for convolutional neural networks. arXiv preprint, 2016. https:\/\/arXiv.org\/1612.02295"},{"key":"203_CR27","doi-asserted-by":"crossref","unstructured":"Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S. Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, 2017. p 2794\u20132802","DOI":"10.1109\/ICCV.2017.304"},{"key":"203_CR28","first-page":"270","volume-title":"International conference on artificial neural networks","author":"C Tan","year":"2018","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: International conference on artificial neural networks. Cham: Springer; 2018. p. 270\u20139."},{"issue":"10","key":"203_CR29","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2009;22(10):1345\u201359.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"203_CR30","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/0301-4738.37595","volume":"56","author":"R Parikh","year":"2008","unstructured":"Parikh R, Mathai A, Parikh S, Sekhar GC, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol. 2008;56(1):45.","journal-title":"Indian J Ophthalmol"},{"key":"203_CR31","unstructured":"Simonyan, K., & Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014. https:\/\/arXiv.org\/1409.1556"},{"key":"203_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"203_CR33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. p. 770\u20138","DOI":"10.1109\/CVPR.2016.90"},{"key":"203_CR34","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, 2017. https:\/\/arXiv.org\/1704.04861"},{"issue":"1","key":"203_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00110-y","volume":"8","author":"DA Abdullah","year":"2020","unstructured":"Abdullah DA, Akp\u0131nar MH, \u015eeng\u00fcr A. Local feature descriptors based ECG beat classification. Health Inf Sci Syst. 2020;8(1):1\u201310.","journal-title":"Health Inf Sci Syst"},{"issue":"1","key":"203_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00125-5","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki R, Ahmed K, Wang H, Zhang Y. Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst. 2020;8(1):1\u20139.","journal-title":"Health Inf Sci Syst"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00203-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-022-00203-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00203-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T10:10:18Z","timestamp":1702635018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-022-00203-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,2]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["203"],"URL":"https:\/\/doi.org\/10.1007\/s13755-022-00203-w","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,2]]},"assertion":[{"value":"22 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We the authors declare that there is no conflict of interest in our manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"3"}}