{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T20:39:02Z","timestamp":1775939942789,"version":"3.50.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01283-8","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T16:02:56Z","timestamp":1727712176000},"page":"1403-1433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7949-9213","authenticated-orcid":false,"given":"Sara","family":"Bouhafra","sequence":"first","affiliation":[]},{"given":"Hassan","family":"El Bahi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"1283_CR1","doi-asserted-by":"crossref","unstructured":"Akter, Atika, et al. \u201cRobust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor.\u201d Expert Systems with Applications (2023): 122347.","DOI":"10.1016\/j.eswa.2023.122347"},{"key":"1283_CR2","unstructured":"Kayode, A. A., et al. \u201cBrain Tumor: An overview of the basic clinical manifestations and treatment.\u201d (2020)."},{"key":"1283_CR3","first-page":"1231","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis,D.N. et al.The 2021 WHO classification of tumors of the central nervous system: a summary. NeuroOncol.23,1231\u20131251(2021).","journal-title":"NeuroOncol."},{"key":"1283_CR4","unstructured":"Watson, Charles, Matthew Kirkcaldie, and George Paxinos. The brain: an introduction to functional neuroanatomy. Academic Press, 2010."},{"key":"1283_CR5","doi-asserted-by":"crossref","unstructured":"Bondy, Melissa L., et al. \u201cBrain tumor epidemiology: consensus from the Brain Tumor Epidemiology Consortium.\u201d Cancer 113.S7 (2008): 1953-1968.","DOI":"10.1002\/cncr.23741"},{"issue":"4","key":"1283_CR6","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/s00018-007-6342-5","volume":"64","author":"M Nakada","year":"2007","unstructured":"M. Nakada, S. Nakada, T. Demuth, N. Tran, D. Hoelzinger and M. Berens, \u201cMolecular targets of glioma invasion.,\u201d Cell and molecular life sciences, vol. 64, no. 4, pp. 458-478, 2007.","journal-title":"Cell and molecular life sciences"},{"issue":"1","key":"1283_CR7","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1038\/s41598-022-05572-6","volume":"12","author":"S Chatterjee","year":"2022","unstructured":"S Chatterjee, FA Nizamani, A N\u00fcrnberger and O Speck, \u201cClassification of brain tumours in MR images using deep spatiospatial models\u201d, Scientific Reports., vol. 12, no. 1, pp. 1505, Jan 2022.","journal-title":"Scientific Reports."},{"key":"1283_CR8","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11060-022-04148-8","volume":"161","author":"MJ Mair","year":"2022","unstructured":"Mair M. J., Berghoff A. S., Brastianos P. K., Preusser M. (2022). Emerging systemic treatment options in meningioma. J. Neuro-Oncol. 161, 245-258. https:\/\/doi.org\/10.1007\/s11060-022-04148-8","journal-title":"J. Neuro-Oncol."},{"key":"1283_CR9","doi-asserted-by":"publisher","unstructured":"Tsukamoto T, Miki Y. Imaging of pituitary tumors: an update with the 5th WHO Classifications-part 1. Pituitary neuroendocrine tumor (PitNET)\/pituitary adenoma. Jpn J Radiol. 2023. https:\/\/doi.org\/10.1007\/s11604-023-01400-7","DOI":"10.1007\/s11604-023-01400-7"},{"key":"1283_CR10","unstructured":"Hospital, Robert Wood Johnson University, \u201cTreatment for Brain Tumors Using the Gamma Knife,\u201d Available: https:\/\/www.rwjbh.org\/rwj-university-hospital-newbrunswick\/treatment-care\/gamma-knife\/conditions-treated\/brain-tumors\/"},{"key":"1283_CR11","doi-asserted-by":"crossref","unstructured":"Khan, Md Saikat Islam, et al. \u201cAccurate brain tumor detection using deep convolutional neural network.\u201d Computational and Structural Biotechnology Journal 20 (2022): 4733-4745.","DOI":"10.1016\/j.csbj.2022.08.039"},{"issue":"2","key":"1283_CR12","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1056\/NEJM200101113440207","volume":"344","author":"Lisa M DeAngelis","year":"2001","unstructured":"DeAngelis, Lisa M. \u201cBrain tumors.\u201d New England journal of medicine 344.2 (2001): 114-123.","journal-title":"New England journal of medicine"},{"key":"1283_CR13","doi-asserted-by":"crossref","unstructured":"Wong, Koon-Pong, et al. \u201cSegmentation of dynamic PET images using cluster analysis.\u201d IEEE Transactions on nuclear science 49.1 (2002): 200-207.","DOI":"10.1109\/TNS.2002.998752"},{"key":"1283_CR14","doi-asserted-by":"crossref","unstructured":"Hammad, Mohamed, et al. \u201cEfficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.\u201d Cancers 15.10 (2023): 2837.","DOI":"10.3390\/cancers15102837"},{"key":"1283_CR15","doi-asserted-by":"crossref","unstructured":"Sawlani, Vijay, et al. \u201cMultiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions.\u201d Insights into imaging 11 (2020): 1-19.","DOI":"10.1186\/s13244-020-00888-1"},{"key":"1283_CR16","doi-asserted-by":"crossref","unstructured":"Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. \u201cReducing the dimensionality of data with neural networks.\u201d science 313.5786 (2006): 504-507.","DOI":"10.1126\/science.1127647"},{"key":"1283_CR17","doi-asserted-by":"publisher","unstructured":"M. Dweik et R. Ferretti, Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging , Neurosci. Inform., vol. 2, no 3, p. 100095, sept. 2022, https:\/\/doi.org\/10.1016\/j.neuri.2022.100095.","DOI":"10.1016\/j.neuri.2022.100095"},{"key":"1283_CR18","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105. 2012."},{"key":"1283_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110055","volume":"136","author":"GS Nijaguna","year":"2023","unstructured":"Nijaguna, G. S., et al. \u201cQuantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis.\u201d Applied Soft Computing 136 (2023): 110055.","journal-title":"Applied Soft Computing"},{"key":"1283_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104223","volume":"80","author":"Jyotismita Chaki","year":"2023","unstructured":"Chaki, Jyotismita, and Marcin Wo\u017aniak. \u201cDeep learning for neurodegenerative disorder (2016 to 2022): A systematic review.\u201d Biomedical Signal Processing and Control 80 (2023): 104223.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1283_CR21","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, \u201cDeep Residual Learning for Image Recognition,\u201d In. Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1283_CR22","doi-asserted-by":"crossref","unstructured":"Huang, Gao, et al. \u201cDensely connected convolutional networks.\u201d Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1283_CR23","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, Akshay Bhuvaneswari, et al. \u201cOptimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization.\u201d Informatics in Medicine Unlocked (2023): 101436.","DOI":"10.1016\/j.imu.2023.101436"},{"key":"1283_CR24","doi-asserted-by":"crossref","unstructured":"Ahmed, Mumtaz, et al. \u201cAn inception V3 approach for malware classification using machine learning and transfer learning.\u201d International Journal of Intelligent Networks 4 (2023): 11-18.","DOI":"10.1016\/j.ijin.2022.11.005"},{"key":"1283_CR25","unstructured":"Vaswani, Ashish, et al. \u201cAttention is all you need. NIPS (2017).\u201d arXiv preprint arXiv:1706.03762 10 (2017): S0140525X16001837."},{"key":"1283_CR26","unstructured":"Dosovitskiy, Alexey, et al. \u201cAn image is worth 16x16 words: Transformers for image recognition at scale.\u201d arXiv preprint arXiv:2010.11929 (2020)."},{"key":"1283_CR27","doi-asserted-by":"crossref","unstructured":"Raja, PM Siva. \u201cBrain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach.\u201d Biocybernetics and Biomedical Engineering 40.1 (2020): 440-453.","DOI":"10.1016\/j.bbe.2020.01.006"},{"issue":"5","key":"1283_CR28","doi-asserted-by":"publisher","first-page":"8727","DOI":"10.3934\/mbe.2023383","volume":"20","author":"R Bhavani","year":"2023","unstructured":"Bhavani, R., and K. Vasanth. \u201cBrain image fusion-based tumour detection using grey level co-occurrence matrix Tamura feature extraction with backpropagation network classification.\u201d Mathematical Biosciences and Engineering 20.5 (2023): 8727-8744.","journal-title":"Mathematical Biosciences and Engineering"},{"key":"1283_CR29","doi-asserted-by":"crossref","unstructured":"Sahoo, Akshya Kumar, et al. \u201cAn improved DNN with FFCM method for multimodal brain tumor segmentation.\u201d Intelligent Systems with Applications (2023): 200245.","DOI":"10.1016\/j.iswa.2023.200245"},{"key":"1283_CR30","doi-asserted-by":"publisher","first-page":"2468","DOI":"10.1016\/j.procs.2023.01.222","volume":"218","author":"Suraj Patil","year":"2023","unstructured":"Patil, Suraj, and Dnyaneshwar Kirange. \u201cEnsemble of Deep Learning Models for Brain Tumor Detection.\u201d Procedia Computer Science 218 (2023): 2468-2479.","journal-title":"Procedia Computer Science"},{"key":"1283_CR31","doi-asserted-by":"crossref","unstructured":"Wong KP (2005) Medical image segmentation: methods and applications in functional imaging. In: Handbook of biomedical image analysis, pp 111-182","DOI":"10.1007\/0-306-48606-7_3"},{"key":"1283_CR32","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Varela, Emilio, et al. \u201cFully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images.\u201d Computerized Medical Imaging and Graphics 104 (2023): 102172.","DOI":"10.1016\/j.compmedimag.2022.102172"},{"key":"1283_CR33","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s13735-018-0162-2","volume":"8","author":"S Saman","year":"2019","unstructured":"Saman, S., & Jamjala Narayanan, S. (2019). Survey on brain tumor segmentation and feature extraction of MR images. International journal of multimedia information retrieval, 8, 79-99.","journal-title":"International journal of multimedia information retrieval"},{"key":"1283_CR34","doi-asserted-by":"crossref","unstructured":"Shemanto, Tanber Hasan, Lubaba Binte Billah, and Md Abrar Ibtesham. \u201cA Novel Method of Thresholding for Brain Tumor Segmentation and Detection.\u201d Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022. Singapore: Springer Nature Singapore, 2023.","DOI":"10.1007\/978-981-19-7528-8_22"},{"key":"1283_CR35","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.439","volume":"219","author":"S Jardim","year":"2023","unstructured":"Jardim, S., Ant\u00f3nio, J., & Mora, C. (2023). Image thresholding approaches for medical image segmentation-short literature review. Procedia Computer Science, 219, 1485-1492.","journal-title":"Procedia Computer Science"},{"key":"1283_CR36","doi-asserted-by":"crossref","unstructured":"Tandel, Gopal S., et al. \u201cRole of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.\u201d Diagnostics 13.3 (2023): 481.","DOI":"10.3390\/diagnostics13030481"},{"key":"1283_CR37","doi-asserted-by":"crossref","unstructured":"Solanki, Shubhangi, et al. \u201cBrain Tumor Detection and Classification using Intelligence Techniques: An Overview.\u201d IEEE Access (2023).","DOI":"10.1109\/ACCESS.2023.3242666"},{"key":"1283_CR38","doi-asserted-by":"crossref","unstructured":"Havaei, Mohammad, et al. \u201cBrain tumor segmentation with deep neural networks.\u201d Medical image analysis 35 (2017): 18-31.","DOI":"10.1016\/j.media.2016.05.004"},{"issue":"5","key":"1283_CR39","doi-asserted-by":"publisher","first-page":"7117","DOI":"10.1007\/s11042-022-13636-y","volume":"82","author":"Adesh Kumar","year":"2023","unstructured":"Kumar, Adesh. \u201cStudy and analysis of different segmentation methods for brain tumor MRI application.\u201d Multimedia Tools and Applications 82.5 (2023): 7117-7139.","journal-title":"Multimedia Tools and Applications"},{"key":"1283_CR40","doi-asserted-by":"crossref","unstructured":"Sultan, Haseeb, et al. \u201cMDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data.\u201d Journal of King Saud University-Computer and Information Sciences 35.5 (2023): 101560.","DOI":"10.1016\/j.jksuci.2023.101560"},{"key":"1283_CR41","doi-asserted-by":"crossref","unstructured":"Sangui, Smarta, et al. \u201c3D MRI Segmentation using U-Net Architecture for the detection of Brain Tumor.\u201d Procedia Computer Science 218 (2023): 542-553.","DOI":"10.1016\/j.procs.2023.01.036"},{"key":"1283_CR42","doi-asserted-by":"publisher","unstructured":"Kesav, N.; Jibukumar, M.G. Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN. J. King Saud Univ. Comput. Inf. Sci. 2021, 33, 1-14. https:\/\/doi.org\/10.1016\/J.JKSUCI.2021.05.008","DOI":"10.1016\/J.JKSUCI.2021.05.008"},{"key":"1283_CR43","doi-asserted-by":"crossref","unstructured":"Masood, Momina, et al. \u201cA novel deep learning method for recognition and classification of brain tumors from MRI images.\u201d Diagnostics 11.5 (2021): 744.","DOI":"10.3390\/diagnostics11050744"},{"key":"1283_CR44","doi-asserted-by":"publisher","first-page":"19909","DOI":"10.1007\/s11042-021-10637-1","volume":"80","author":"Fatemh Bashir-Gonbadi","year":"2021","unstructured":"Bashir-Gonbadi, Fatemh, and Hassan Khotanlou. \u201cBrain tumor classification using deep convolutional autoencoder-based neural network: Multi-task approach.\u201d Multimedia Tools and Applications 80 (2021): 19909-19929.","journal-title":"Multimedia Tools and Applications"},{"key":"1283_CR45","doi-asserted-by":"crossref","unstructured":"Nayak, Dillip Ranjan, et al. \u201cA deep autoencoder approach for detection of brain tumor images.\u201d Computers and Electrical Engineering 102 (2022): 108238.","DOI":"10.1016\/j.compeleceng.2022.108238"},{"key":"1283_CR46","doi-asserted-by":"crossref","unstructured":"Waghere, Sandhya Sandeep, and Jayashri Prashant Shinde. \u201cA robust classification of brain tumor disease in MRI using twin-attention based dense convolutional auto-encoder.\u201d Biomedical Signal Processing and Control 92 (2024): 106088.","DOI":"10.1016\/j.bspc.2024.106088"},{"key":"1283_CR47","doi-asserted-by":"crossref","unstructured":"Ramasamy, Gayathri, Tripty Singh, and Xiaohui Yuan. \u201cMulti-Modal Semantic Segmentation Model using Encoder Based Link-Net Architecture for BraTS 2020 Challenge.\u201d Procedia Computer Science 218 (2023): 732-740.","DOI":"10.1016\/j.procs.2023.01.053"},{"key":"1283_CR48","doi-asserted-by":"crossref","unstructured":"Chawla, Riddhi, et al. \u201cBrain tumor recognition using an integrated bat algorithm with a convolutional neural network approach.\u201d Measurement: Sensors 24 (2022): 100426.","DOI":"10.1016\/j.measen.2022.100426"},{"key":"1283_CR49","doi-asserted-by":"crossref","unstructured":"Yaqub, Muhammad, et al. \u201cDeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor.\u201d Alexandria Engineering Journal 76 (2023): 609-627.","DOI":"10.1016\/j.aej.2023.06.062"},{"key":"1283_CR50","doi-asserted-by":"crossref","unstructured":"Talukder, Md Alamin, et al. \u201cAn efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning.\u201d Expert Systems with Applications (2023): 120534.","DOI":"10.1016\/j.eswa.2023.120534"},{"key":"1283_CR51","doi-asserted-by":"crossref","unstructured":"Kaur, Manjit, et al. \u201cEfficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.\u201d Diagnostics 13.20 (2023): 3234.","DOI":"10.3390\/diagnostics13203234"},{"key":"1283_CR52","doi-asserted-by":"crossref","unstructured":"Abdusalomov, Akmalbek Bobomirzaevich, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo. \u201cBrain tumor detection based on deep learning approaches and magnetic resonance imaging.\u201d Cancers 15.16 (2023): 4172.","DOI":"10.3390\/cancers15164172"},{"issue":"1","key":"1283_CR53","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s44196-022-00090-9","volume":"15","author":"Wen Jun","year":"2022","unstructured":"Jun, Wen, and Zheng Liyuan. \u201cBrain Tumor Classification Based on Attention Guided Deep Learning Model.\u201d International Journal of Computational Intelligence Systems 15.1 (2022): 35.","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"1283_CR54","doi-asserted-by":"crossref","unstructured":"Xu, Qing, et al. \u201cDCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation.\u201d Computers in Biology and Medicine 154 (2023): 106626.","DOI":"10.1016\/j.compbiomed.2023.106626"},{"key":"1283_CR55","doi-asserted-by":"crossref","unstructured":"Tang, Chaosheng, et al. \u201cGAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification.\u201d Journal of King Saud University-Computer and Information Sciences 35.2 (2023) : 560-575.","DOI":"10.1016\/j.jksuci.2023.01.002"},{"key":"1283_CR56","doi-asserted-by":"crossref","unstructured":"Alzahrani, Salha M., and Abdulrahman M. Qahtani. \u201cKnowledge Distillation in Transformers with Tripartite Attention: Multiclass Brain Tumor Detection in Highly Augmented MRIs.\u201d Journal of King Saud University-Computer and Information Sciences (2023): 101907.","DOI":"10.1016\/j.jksuci.2023.101907"},{"key":"1283_CR57","unstructured":"Wang, Wenxuan, et al. \u201cTransbts: Multimodal brain tumor segmentation using transformer.\u201d Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part I 24. Springer International Publishing, 2021."},{"key":"1283_CR58","doi-asserted-by":"crossref","unstructured":"Alzahrani, Salha M. \u201cConvAttenMixer: Brain tumor detection and type classification using convolutional mixer with external and self-attention mechanisms.\u201d Journal of King Saud University-Computer and Information Sciences 35.10 (2023): 101810.","DOI":"10.1016\/j.jksuci.2023.101810"},{"key":"1283_CR59","doi-asserted-by":"crossref","unstructured":"Dutta, Tapas Kumar, Deepak Ranjan Nayak, and Yu-Dong Zhang. \u201cArm-net: Attention-guided residual multiscale cnn for multiclass brain tumor classification using mr images.\u201d Biomedical Signal Processing and Control 87 (2024): 105421.","DOI":"10.1016\/j.bspc.2023.105421"},{"key":"1283_CR60","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.neucom.2022.06.107","volume":"503","author":"Lalita Mishra","year":"2022","unstructured":"Mishra, Lalita, and Shekhar Verma. \u201cGraph attention autoencoder inspired CNN based brain tumor classification using MRI.\u201d Neurocomputing 503 (2022): 236-247.","journal-title":"Neurocomputing"},{"key":"1283_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105938","volume":"91","author":"Emrullah \u015eahin","year":"2024","unstructured":"\u015eahin, Emrullah, Durmu\u015f \u00d6zdemir, and Hasan Temurta\u015f. \u201cMulti-objective optimization of ViT architecture for efficient brain tumor classification.\u201d Biomedical Signal Processing and Control 91 (2024): 105938.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1283_CR62","doi-asserted-by":"crossref","unstructured":"Asiri, Abdullah A., et al. \u201cAdvancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.\u201d Sensors 23.18 (2023): 7913.","DOI":"10.3390\/s23187913"},{"key":"1283_CR63","unstructured":"Goodfellow, Ian, et al. \u201cGenerative adversarial nets.\u201d Advances in neural information processing systems 27 (2014)."},{"issue":"12","key":"1283_CR64","doi-asserted-by":"publisher","first-page":"16441","DOI":"10.1007\/s11042-022-12362-9","volume":"81","author":"Wessam M Salama","year":"2022","unstructured":"Salama, Wessam M., and Ahmed Shokry. \u201cA novel framework for brain tumor detection based on convolutional variational generative models.\u201d Multimedia Tools and Applications 81.12 (2022): 16441-16454.","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"1283_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2022.100060","volume":"2","author":"Arkapravo Chattopadhyay","year":"2022","unstructured":"Chattopadhyay, Arkapravo, and Mausumi Maitra. \u201cMRI-based brain tumour image detection using CNN based deep learning method.\u201d Neuroscience informatics 2.4 (2022): 100060.","journal-title":"Neuroscience informatics"},{"key":"1283_CR66","unstructured":"Nassar, Shaimaa E., et al. \u201cA robust MRI-based brain tumor classification via a hybrid deep learning technique.\u201d The Journal of Supercomputing (2023): 1-25."},{"key":"1283_CR67","doi-asserted-by":"crossref","unstructured":"Rajasree, R., C. Christopher Columbus, and C. Shilaja. \u201cMultiscale-based multimodal image classification of brain tumor using deep learning method.\u201d Neural Computing and Applications 33 (2021): 5543-5553.","DOI":"10.1007\/s00521-020-05332-5"},{"key":"1283_CR68","doi-asserted-by":"crossref","unstructured":"Saiful, Md, et al. \u201cMRI-Based Brain Tumor Classification Using Various Deep Learning Convolutional Networks and CNN.\u201d International Conference on Intelligent Computing & Optimization. Cham: Springer Nature Switzerland, 2023.","DOI":"10.1007\/978-3-031-36246-0_17"},{"key":"1283_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105716","volume":"89","author":"A Priya","year":"2024","unstructured":"Priya, A., and V. Vasudevan. \u201cBrain tumor classification and detection via hybrid alexnet-gru based on deep learning.\u201d Biomedical Signal Processing and Control 89 (2024): 105716.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1283_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105459","volume":"87","author":"Monika Sachdeva","year":"2024","unstructured":"Sachdeva, Monika, and Alok Kumar Singh Kushwaha. \u201cIRNetv: A deep learning framework for automated brain tumor diagnosis.\u201d Biomedical Signal Processing and Control 87 (2024): 105459.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1283_CR71","doi-asserted-by":"crossref","unstructured":"Sandhiya, B., and S. Kanaga Suba Raja. \u201cDeep Learning and Optimized Learning Machine for Brain Tumor Classification.\u201d Biomedical Signal Processing and Control 89 (2024): 105778.","DOI":"10.1016\/j.bspc.2023.105778"},{"key":"1283_CR72","doi-asserted-by":"crossref","unstructured":"Xu, Lu, and Morteza Mohammadi. \u201cBrain tumor diagnosis from MRI based on Mobilenetv2 optimized by contracted fox optimization algorithm.\u201d Heliyon 10.1 (2024).","DOI":"10.1016\/j.heliyon.2023.e23866"},{"key":"1283_CR73","doi-asserted-by":"crossref","unstructured":"Vankdothu, Ramdas, and Mohd Abdul Hameed. \u201cBrain tumor MRI images identification and classification based on the recurrent convolutional neural network.\u201d Measurement: Sensors 24 (2022): 100412.","DOI":"10.1016\/j.measen.2022.100412"},{"key":"1283_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105119","volume":"86","author":"Sadafossadat Tabatabaei","year":"2023","unstructured":"Tabatabaei, Sadafossadat, Khosro Rezaee, and Min Zhu. \u201cAttention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system.\u201d Biomedical Signal Processing and Control 86 (2023): 105119.","journal-title":"Biomedical Signal Processing and Control"},{"key":"1283_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101940","volume":"91","author":"Maria Nazir","year":"2021","unstructured":"Nazir, Maria, Sadia Shakil, and Khurram Khurshid. \u201cRole of deep learning in brain tumor detection and classification (2015 to 2020): A review.\u201d Computerized medical imaging and graphics 91 (2021): 101940.","journal-title":"Computerized medical imaging and graphics"},{"key":"1283_CR76","unstructured":": Kazerooni, Anahita Fathi, et al. \u201cThe Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).\u201d ArXiv (2023)."},{"key":"1283_CR77","unstructured":"\u2018Multimodal Brain Tumor Segmentation Challenge 2020: Previous BraTS Instances\u2019. [Online]. Available: http:\/\/braintumorsegmentation.org\/"},{"key":"1283_CR78","unstructured":"BrainWeb Dataset.\u2019 [Online]. Available: https:\/\/brainweb.bic.mni.mcgill.ca\/"},{"key":"1283_CR79","unstructured":"\u2018Brain tumor dataset\u2019. [Online]. Available: https:\/\/figshare.com\/articles\/brain_tumor_dataset\/1512427."},{"key":"1283_CR80","unstructured":"Johnson, M.D.Keith A., Alex Becker, P.D.J.. The Whole Brain Atlas\u2019. [Online]. Available: http:\/\/www.med.harvard.edu\/AANLIB\/."},{"key":"1283_CR81","unstructured":"Brain MRI Images for Brain Tumor Detection\u2019. [Online]. Available: https:\/\/www.kaggle.com\/navoneel\/brain-mri-images-for-brain-tumor-detection."},{"key":"1283_CR82","unstructured":"IXI Dataset.\u2019 [Online]. Available: https:\/\/brain-development.org\/ixi-dataset\/"},{"key":"1283_CR83","unstructured":"IXI: brain-development website (2019) https:\/\/brain-development.org\/ixi-dataset Accessed: 2019-05-30"},{"key":"1283_CR84","unstructured":"IBSR Dataset. [Online]. Available: https:\/\/www.nitrc.org\/projects\/ibsr"},{"key":"1283_CR85","unstructured":"\u2018Rider neuro MRI\u2019. [Online]. Available: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/RIDER+NEURO+MRI"},{"key":"1283_CR86","unstructured":"\u2018THE Cancer IMAGING ARCHIVE (TCIA)\u2019. [Online]. Available: https:\/\/www.cancerimagingarchive.net\/collections\/."},{"key":"1283_CR87","doi-asserted-by":"crossref","unstructured":"Alrashedy, Halima Hamid N., et al. \u201cBrainGAN: brain MRI image generation and classification framework using GAN architectures and CNN models.\u201d Sensors 22.11 (2022): 4297.","DOI":"10.3390\/s22114297"},{"key":"1283_CR88","unstructured":"Reynolds R, Grant G. Youmans Neurological Surgery. 8th ed. Philadelphia, PA: Elsevier Inc; 2022. General approaches and considerations for pediatric brain tumors."},{"key":"1283_CR89","doi-asserted-by":"publisher","unstructured":"Mengide JP, Berros MF, Turza ME, Li\u00f1ares JM. Posterior fossa tumors in children: An update and new concepts. Surg Neurol Int. 2023 Mar 31;14:114. https:\/\/doi.org\/10.25259\/SNI_43_2023. PMID: 37151431; PMCID: PMC10159277.\u201d","DOI":"10.25259\/SNI_43_2023"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01283-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01283-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01283-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T17:27:38Z","timestamp":1747762058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01283-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":89,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["1283"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01283-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"20 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors involved have agreed to participate in this submitted article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The selected pictures of sample cases have personal consent form of each patient.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publicaton"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}