Most cited papers in Brain Tumor Detection and Classification
What are the most cited papers in Brain Tumor Detection and Classification?
The most cited paper in Brain Tumor Detection and Classification is "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" (Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng et al., 2018), with 6,903 citations. Next come "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)" (6,843) and "Brain tumor segmentation with Deep Neural Networks" (3,312).
Showing 50 of 100 papers. Every paper with more than 542 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng et al. · Lecture notes in computer science | 2018 | 6,903 |
| 2 | The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Bjoern H. Menze et al. · IEEE Transactions on Medical Imaging | 4 Dec 2014 | 6,843 |
| 3 | Brain tumor segmentation with Deep Neural Networks Mohammad Havaei, Axel Davy, David Warde-Farley et al. · Medical Image Analysis | 21 May 2016 | 3,312 |
| 4 | A Comprehensive Survey on Graph Neural Networks Wu, Z, Pan, S, Chen, F et al. · UTS ePRESS (University of Technology Sydney) | 24 Mar 2020 | 3,301 |
| 5 | Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images Sérgio Pereira, Adriano B. Pinto, Victor Alves et al. · IEEE Transactions on Medical Imaging | 4 Mar 2016 | 2,750 |
| 6 | Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations Carole H. Sudre, Wenqi Li, Tom K. Vercauteren et al. · Lecture notes in computer science | 2017 | 2,701 |
| 7 | MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation Nabil Ibtehaz, M. Sohel Rahman · Neural Networks | 4 Sep 2019 | 2,356 |
| 8 | An overview of deep learning in medical imaging focusing on MRI Alexander Selvikvåg Lundervold, Alexander Selvikvåg Lundervold, Arvid Lundervold et al. · Zeitschrift für Medizinische Physik | 13 Dec 2018 | 2,049 |
| 9 | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang et al. · Lecture notes in computer science | 2022 | 1,898 |
| 10 | Hypergraph Neural Networks Yifan Feng, Haoxuan You, Zizhao Zhang et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 17 Jul 2019 | 1,743 |
| 11 | A Semiautomated Method for Measuring Brain Infarct Volume Raymond A. Swanson, Matthew T. Morton, George Tsao-Wu et al. · Journal of Cerebral Blood Flow & Metabolism | Mar 1990 | 1,687 |
| 12 | Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs Xiaohan Ding, Xiangyu Zhang, Jungong Han et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 1,453 |
| 13 | Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Spyridon Bakas et al. · arXiv (Cornell University) | 5 Nov 2018 | 1,422 |
| 14 | Brain tumor classification using deep CNN features via transfer learning S. Deepak, P. M. Ameer · Computers in Biology and Medicine | 29 Jun 2019 | 1,323 |
| 15 | Recursive Partitioning Analysis of Prognostic Factors in Three Radiation Therapy Oncology Group Malignant Glioma Trials W.J. Curran, Charles Scott, John Horton et al. · JNCI Journal of the National Cancer Institute | 5 May 1993 | 1,318 |
| 16 | Medical Image Analysis using Convolutional Neural Networks: A Review Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum et al. · Journal of Medical Systems | 8 Oct 2018 | 1,271 |
| 17 | End-To-End Multi-Task Learning With Attention Shikun Liu, Edward Johns, Andrew J. Davison | Jun 2019 | 1,204 |
| 18 | Automatic classification of MR scans in Alzheimer's disease Stefan Klöppel, Cynthia M. Stonnington, Carlton Chu et al. · Brain | 17 Jan 2008 | 1,189 |
| 19 | Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi et al. · Journal of Imaging Informatics in Medicine | 2 Jun 2017 | 1,144 |
| 20 | Improved Knowledge Distillation via Teacher Assistant Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 3 Apr 2020 | 1,096 |
| 21 | Classification using deep learning neural networks for brain tumors Heba Mohsen, El‐Sayed A. El‐Dahshan, El-Sayed M. El-Horbaty et al. · Future Computing and Informatics Journal | 23 Dec 2017 | 1,082 |
| 22 | A survey of MRI-based medical image analysis for brain tumor studies Stefan M. Bauer, Roland Wiest, Lutz-P. Nolte et al. · Physics in Medicine and Biology | 6 Jun 2013 | 1,046 |
| 23 | Support vector machine Derek Pisner, David M. Schnyer · Machine Learning | 15 Nov 2019 | 1,005 |
| 24 | Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis Heung‐Il Suk, Seong–Whan Lee, Dinggang G. Shen · NeuroImage | 18 Jul 2014 | 997 |
| 25 | EIE: Efficient Inference Engine on Compressed Deep Neural Network Song Han, Xingyu Liu, Huizi Mao et al. | Jun 2016 | 988 |
| 26 | A review of medical image data augmentation techniques for deep learning applications Phillip Chlap, Hang Min, Nym Vandenberg et al. · Journal of Medical Imaging and Radiation Oncology | 19 Jun 2021 | 979 |
| 27 | Transfer learning for medical image classification: a literature review Kim Eun Hee, Alejandro Cosa‐Linan, Nandhini Santhanam et al. · BMC Medical Imaging | 13 Apr 2022 | 976 |
| 28 | TransBTS: Multimodal Brain Tumor Segmentation Using Transformer Wenxuan Wang, Chen Chen, Meng Ding et al. · Lecture notes in computer science | 2021 | 969 |
| 29 | Review of MR image segmentation techniques using pattern recognition James C. Bezdek, Lawrence Hall, Laurence P. Clarke · Medical Physics | Jul 1993 | 956 |
| 30 | Conformity index: A review Loic Feuvret, Georges Noël, Jean‐Jacques Mazeron et al. · International Journal of Radiation Oncology*Biology*Physics | 19 Jan 2006 | 948 |
| 31 | Convolutional neural networks in medical image understanding: a survey D. R. Sarvamangala, Raghavendra Vinayakarao Kulkarni · Evolutionary Intelligence | 3 Jan 2021 | 920 |
| 32 | Multi-grade brain tumor classification using deep CNN with extensive data augmentation Muhammad Husain As Sajjad, Salman Khan, Khan Muhammad et al. · Journal of Computational Science | 12 Dec 2018 | 900 |
| 33 | CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope Dulari Bhatt, Chirag I Patel, Hardik Talsania et al. · Electronics | 11 Oct 2021 | 887 |
| 34 | Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters Katja Franke, Gabriel Ziegler, Stefan Klöppel et al. · NeuroImage | 12 Jan 2010 | 876 |
| 35 | Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition Jun Ting Cheng, Wei Huang, Shuangliang Cao et al. · PLoS ONE | 8 Oct 2015 | 867 |
| 36 | Recurrent residual U-Net for medical image segmentation Md Zahangir Alom, Chris Yakopcic, Mahmudul Hasan et al. · Journal of medical imaging | 27 Mar 2019 | 864 |
| 37 | Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme Evangelia I. Zacharaki, Sumei Wang, Sanjeev Chawla et al. · Magnetic Resonance in Medicine | 26 Oct 2009 | 863 |
| 38 | Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks Hao Dong, Guang Zhong Yang, Fangde Liu et al. · Communications in computer and information science | 2017 | 850 |
| 39 | Brain tumor classification for MR images using transfer learning and fine-tuning Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir et al. · Computerized Medical Imaging and Graphics | 18 May 2019 | 841 |
| 40 | Medical image segmentation using deep learning: A survey Risheng Wang, Tao Lei, Ruixia Cui et al. · IET Image Processing | 17 Jan 2022 | 826 |
| 41 | A deep learning model integrating FCNNs and CRFs for brain tumor segmentation Xiaomei Zhao, Yihong Wu, Guidong Song et al. · Medical Image Analysis | 11 Oct 2017 | 825 |
| 42 | Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation Junhao Wen, Elina Thibeau–Sutre, Mauricio Diaz-Melo et al. · Medical Image Analysis | 30 Apr 2020 | 823 |
| 43 | Multi-modality image registration by maximization of mutual information Frederik Maes, Andre M. F. Collignon, Dirk Vandermeulen et al. | 1996 | 804 |
| 44 | VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images Hao Chen, Qi Dou, Lequan Yu et al. · NeuroImage | 23 Apr 2017 | 800 |
| 45 | Hypergraph convolution and hypergraph attention Song Bai, Feihu Zhang, Philip H. S. Torr · Pattern Recognition | 14 Sep 2020 | 797 |
| 46 | Adaptive Graph Convolutional Neural Networks Ruoyu Li, Sheng Wang, Feiyun Zhu et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 789 |
| 47 | TransMorph: Transformer for unsupervised medical image registration Junyu Chen, Eric C. Frey, Yufan He et al. · Medical Image Analysis | 14 Sep 2022 | 782 |
| 48 | Multi-Classification of Brain Tumor Images Using Deep Neural Network Hossam H. Sultan, Nancy M. Salem, Walid Al‐Atabany · IEEE Access | 2019 | 774 |
| 49 | Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods Ali Işın, Cem Direkoğlu, Melike Şah · Procedia Computer Science | 2016 | 769 |
| 50 | Segment anything model for medical image analysis: An experimental study Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu et al. · Medical Image Analysis | 2 Aug 2023 | 767 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.