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Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Open accessAbstract
Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.
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“Data collected from three different kinds of brain tumors consisted of 708 meningiomas, 1426 gliomas, and 930 pituitaries images from the General Hospital and Nanfang Hospital, Tianjin Medical University, China [72].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
In the citing paper
“This contains 3064 T1-weighted contrast-enhanced images collected from 233 patients with a resolution of 512 × 512 pixels.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
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Cites
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“(cid:129) Local features: The next step is to extract local features within the ROI, such as scale-invariant feature transform (SIFT) descriptor [20] and raw patch [21].”
Background$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
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“We also compared three classification methods (i.e. SVM, sparse representation-based classification (SRC) [31], and k nearest neighbors (kNN) classifier) using 2-region BoW representations.”
ResultBeyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · cited 4×
“Among those methods, the most notable work is SPM [15], which splits the image into hierarchical cells, computes BoW representation for each cell, and finally weights and concatenates the results.”
BackgroundMethodInfluentialVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367 · cited 1×
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Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,760 · cited 1×
“As pointed out in [29,30], it is beneficial to capture a certain amount of context.”
BackgroundA Bayesian Hierarchical Model for Learning Natural Scene Categories
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“The BoW method was originally used in the text retrieval domain, and it has been successfully adapted to the visual analysis domain [13,14].”
MethodLocality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100 · cited 2×
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Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206757 · Cited by 2,895
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Research works printed but not linked: 1
- S Patil, 2013, A Computer Aided Diagnostic System For Classification Of Brain Tumors Using Texture Features And Probabilistic Neural Network, Int J Comput Sci Eng Inf Technol Res, 3, 61
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Nalepa, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_284-1
Nalepa, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_284
Efeito da deterioração em imagens por ressonância magnética sobre redes neurais profundas
dos Prazeres, C. L. S., de Paula, P. L. A. H., Monte, M. N., Alcântara, M. C., dos Santos, E. A. B., & Campos, L. (2022) · Research Society and Development · doi:10.33448/rsd-v11i9.31868
A Research Study on Brain Tumor Detection Techniques
Joseph, N., Murugan, D., & Mohan, D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0976-4_43
رفعت, ف. م. م., محمد, أ. ف., جودة, م. م., & أحمد, م. ع. (2021) · مجلة دراسات تربوية واجتماعية · doi:10.21608/jsu.2021.246715
A Web Based e-health Care Application for Brain Tumor Detection and Analysis
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Brain Tumor Detection in Magnetic Resonance Imaging using Deep Learning ApproachPreprint
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An Optimized Transfer Learning Based Framework for Brain Tumor Classification
Arya, M. K., & Agrawal, R. K. (2022) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.100467
СИСТЕМА АНАЛІЗУ АСИМЕТРІЇ КТ-ЗОБРАЖЕНЬ ДЛЯ УДОСКОНАЛЕННЯ ПРОЦЕДУР ВИЯВЛЕННЯ ПАТОЛОГІЇ
Атамась, В. В., & Настенко, Є. А. (2022) · Біомедична інженерія і технологія · doi:10.20535/2617-8974.2022.8.268507
“Приклади знімків МРТ: a – менінгіоми; b – гліоми; c – пухлини гіпофізу [1-2]”
BackgroundPerformance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification
Chauhan, R., & Aggarwal, G. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6980-4.ch010
Deep Learning Architectures for Accurate Brain Tumour Analysis
Babu, Y. M. M., kishore, D. V. S., Blessy, Y. M., Prabhu, V. S., Uthayakumar, C., & Renukadevi, S. (2022) · International Conference on Computer, Power and Communications (ICCPC) · doi:10.1109/iccpc55978.2022.10072111
“Online [13] NH-GH-TMU [14] 233 3064”
Yuksel, H., Bingöl, H., & Alataş, B. (2023) · Traitement du signal · doi:10.18280/ts.400236
Brain tumor detection in the Spark system
Benkrama, S., & Hemdani, N. E. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v31.i2.pp755-762
“Several DL detection architectures are currently proposed to automatically classify BT [8], [9].”
Early Detection of Brain Tumor using Capsule Network
-, M. A. H., Hasan, M. M., & -, M. F. H. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i03.3264
Evaluation of Convolutional Neural Networks for Automatic Classification of Brain TumorPreprint
Barati, B., Birgani, F. F., Navidifar, T., Mousav, S. A., & Khoshgard, K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4524701
Ingle, A., Roja, M. M., Sankhe, M. S., & Patkar, D. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i6.7301
Suryawanshi, S., & Patil, S. B. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i7.8003
Automated Models for the Classification of Magnetic Resonance Brain Tumour Images
Divya, S., Ali, A., Ibrahim, N., & Suresh, L. P. (2023) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac57885.2023.10275235
“To categorise brain tumour types into gliomas, meningiomas, and pituitary tumours, Cheng et al. [9] created an open brain tumour dataset.”
BackgroundS., D. A. S., Guevara, R. D., & V., S. A. C. (2023) · Journal of Applied Statistics · doi:10.1080/02664763.2023.2279012
“This dataset contains brain MRI images of patients included in The Cancer Genome Atlas (TCGA) glioma and notumor collection, with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available [3].”
A Novel Ensemble Model for Brain Tumor Diagnosis
Talaat, A. S. (2023) · New Mathematics and Natural Computation · doi:10.1142/s1793005725500115
Sharma, A. K., Pathak, S., Geldra, S., Bisht, S., & Sharma, V. K. (2023) · International Conference on Innovative Sustainable Computational Technologies (CISCT) · doi:10.1109/cisct57197.2023.10351469
“In the study conducted by Cheng et al. [15], it was observed that brain tumors display significant variations in terms of their shape, size, and intensity.”
Backgroundde Oliveira, B. V., Dantas, W. M., Vilar, C. D., & Soares, H. B. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-49404-8_19
Brain Tumour Identification and Prediction with Machine Learning
Rajput, P., Pramanik, A., & Lande, J. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10397790
“Gliomas are the most prevalent forms of BT that arise from glial cells in the brain [2].”
BackgroundBrain Tumor Segmentation Using Type-2 Neutrosophic Thresholding Approach
Singh, P. (2023) · Brain informatics and health · doi:10.1007/978-981-99-9939-2_5
Percolation Images: Fractal Geometry Features for Brain Tumor Classification
Lumini, A., Roberto, G. F., Neves, L. A., Martins, A. S., & do Nascimento, M. Z. (2023) · Advances in neurobiology · doi:10.1007/978-3-031-47606-8_29
Detecting Subtypes of Brain Tumors using Resnet50 with Transfer Learning
NARASIMHA, P. K. N., -, J. L. C., Kumar, J., -, K. T., & Almas, S. M. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i02.15579
Brain Tumor Identification using Transfer Learning with Sugeno-Fuzzy IntegralPreprint
Govil, N., Choudhary, S. L., Dixit, R. S., Anand, S., Rachapudi, V., Patro, S. G. K., Channa Keshava Naik, N., Muzammil, K., Islam, S., & Khan, W. A. (2024) · Research Square · doi:10.21203/rs.3.rs-4108109/v1
Qin, C., Cao, Y. G., & Meng, L. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.049228
Malignant tumor identification with custom activated deep CNN architecture: TumorNet
Balakrishnan, A., Preethaa, K. R. S., Yuvaraj, N. R., Kathiresan, K., & Karthikeyan, B. (2024) · AIP conference proceedings · doi:10.1063/5.0203771
Kong, L., Mei, J., Lu, Y., Jia, F., & Du, W. (2024) · doi:10.36227/techrxiv.171779097.73679789/v1
Using Resizing Layer in U-Net to Improve Memory Efficiency
Dénes-Fazakas, L., Csaholczi, S., Eigner, G., Kovács, L., & Szilágyi, L. S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-61857-4_4
Alsini, R. A. I. (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545217
“These metrics provide an overview of the model's performance as detailed in [39] and calculated as follows: • Accuracy : (TP + TN) (TP + TN + FN + FP) • Sensitivity/Recall :”
MethodBrain tumor classification model using convolutional neural networks on magnetic resonance imaging
Leal, L. B., Lima, F. D. C. A., Rabêlo, R. A. L., & Moraes, M. J. A. (2024) · Caderno Pedagógico · doi:10.54033/cadpedv21n9-025
Multi-class Brain Tumour Classification Using Optimized ResNet50
Rajamanickam, S., Iyer, A., & Ushasukhanya, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-68908-6_20
Resident Vision Transformer: Lightweight Deep Learning Model for Disease Diagnosis on Edge Devices
Raj, R., Raut, P. P., Zope, M. K., Mathew, J., Kannath, S. K., & Rajan, J. (2024) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc62041.2024.10690344
“…available datasets, namely, The mini-MIAS database of mammograms [19], PH2 database for Skin Cancer [20] and Brain tumor classification database [21] Methods selected for comparative study: We have selected few models across various categories such as CNNs, Anti-aliased CNNs and Vision…”
MethodKapo, M., Akagić, A., & Buza, E. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708196
“Collected between 2005 and 2010 from Nanfang Hospital in Guangzhou, China, and the General Hospital at Tianjin Medical University, China, these images originate from 233 patients [12].”
BackgroundMethodBagheri, F., milan, A., & Falahi, G. (2024) · نشریه علمی علوم و فنون نقشه برداری · doi:10.61186/jgst.14.1.25
Tumor Detection and Analysis from Brain MRI Images Using Deep Learning
Singh, S., & Srivastava, R. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-4359-9_8
Improved Binarized Statistical Image Features for MRI Brain Tumor Identification and Classification
Chellakh, H., Moussaouı, A., & Attıa, A. (2024) · International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) · doi:10.1109/ict-dm62768.2024.10798938
“Multiclass classification is at times a tedious and challenging problem in comparison with binary classification (e.g., in pathological and non-pathological) [8].”
InfluentialAutomatic Brain Tumor Classification Using Transfer Learning
Pranitha, K., Madhavi, K. R., Pranathi, R., Shaik, A. B., & Vishnu, P. (2024) · Advanced technologies and societal change · doi:10.1007/978-981-99-2832-3_22
Multi-Model Fusion for Brain Tumor Detection: A Hybrid Deep Learning Approach
Shyamala, N., & MahaboobBasha, S. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10860024
A Method for Brain Tumor Segmentation Using DeeplabV3+: Learning Rate Optimization
Piboonthummasak, T., Yamcharoen, N., Nobnop, N., & Charoenpong, T. (2024) · Biomedical Engineering International Conference · doi:10.1109/bmeicon64021.2024.10896264
“Cheng et al.. [3] proposed the automatic classification type of a tissue.”
Deep Learning for Segmentation of Brain Tumors
Tamang, S., Ariyametkul, A., Paing, M. P., & Bui, T. (2024) · Biomedical Engineering International Conference · doi:10.1109/bmeicon64021.2024.10896265
Deep Dynamic Ensemble Classification for Brain Tumor MRI
Wang, J., Chen, W., Yang, J., & Zhong, R. (2024) · International Conference on Communication Technology and Information Technology (ICCTIT) · doi:10.1109/icctit64404.2024.10928401
“These classifiers, such as Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Random Forest, and Decision Tree, rely on well-designed image features to distinguish between different types of brain tumors [5].”
A Deep Learning Approach for Brain Tumor Classification
Abdel-Moniem, A., Ebrahim, G. A., & Abdellatif, M. M. (2024) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca62237.2024.10927964
Sengupta, A., Pal, A. R., & Dasgupta, A. (2025) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect63952.2025.10958960
Elagamy, M., Selim, H., & Elkaffas, S. M. (2023) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta60978.2023.10969194
“…methods for MRI can be categorized into four main categories: the methods exploiting different transformations of the source information, including affine MRI transformations, elastic transformations, pixel-level transformations, and various methods for generating artificial data [18 – 20].”
A Leakage‐Resistant Spatially Weighted Active Contour for Brain Tumor Segmentation
Kumar, B., Agrawal, S., & Panda, R. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70110
Brain Cancer Detection and Classification using Transfer Learning
Gadaokar, V., More, P. A., Wakode, M., & Kale, G. (2025) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi64286.2025.10984561
“A landmark in this regard was made by a study that devised a technique for better classification of brain tumors by region of interest (ROI) enhancement [14], [15].”
Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation
Yavuz, M. C., & Yang, Y. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980764
Robust Multi-class Brain Tumor Classification Using Hybrid Transfer Learning
Saravanan, G., & Jeevanantham, V. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965354217250209232119
Brain Tumor Classification Using Transfer Learning
Nguyen, D.-H., & Huynh, V.-M. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-90197-3_36
Enhanced Brain Tumor Classification using Ensemble Fusion Method
Puja, R. S., & Yousuf, M. A. (2024) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3723178.3723301
“Reference [21] provides public access to this dataset, which was initially utilized in publication [12].”
Brain Tumor Detection–Using Medical Imaging and Machine Learning Techniques: A Python-Based Approach
Tomeshwar, Yadav, K., & Dadsena, S. S. (2025) · doi:10.38124/ijisrt/25may2340
Brain Tumor Detection and Classification Using Attention Based Residual Learning
Sharma, K., Jaiswal, V., Dey, T., Chauhan, S. S., Hati, A. N., & Jangid, M. (2025) · International Conference on Next Generation Communication & Information Processing (INCIP) · doi:10.1109/incip64058.2025.11019958
“This dataset is used in two papers, i.e. J. Cheng et al. [1] and [2].”
Exploring Vision Transformer Architectures for Brain Tumor Classification: A Comprehensive Study
Dénes-Fazakas, L., Simon, B. F., Hartvég, Á., Csaholczi, S., Eigner, G., Kovács, L., & Szilágyi, L. S. (2025) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci66288.2025.11030100
Evaluating the Detection of Small Brain Lesions in Magnetic Resonances Using Deep LearningPreprint
Nogales, A., de la Pinta, C., García-Tejedor, Á. J., García‐Barriocanal, E., Guadalupe, D., & Sicilia, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5364234
Brain Tumor Detection, Segmentation, and Classification Using Computer Vision Techniques
Yin, N. S., & Ponnusamy, R. R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5220-4_2
Interpreting capsule networks for image classification by routing path visualization
Bhullar, A., Czomko, M., Ali, R. A., & Welch, D. L. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104395
Brain tumor classification using convolutional neural network
Minarno, A. E., Mustaqim, M. C., Munarko, Y., & Basuki, S. (2025) · AIP conference proceedings · doi:10.1063/5.0259412
Convolutional neural network for meningioma, glioma, and pituitary brain tumor classification
Minarno, A. E., Azhar, Y., Putra, I. Y., & Wibowo, H. (2025) · AIP conference proceedings · doi:10.1063/5.0277228
Sarita, & Dass, R. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/adf8ef
Kang, H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92608-2_1
Pituitary Tumor Classification Using Convolutional Neural Networks
Madini, M. A., & Algethami, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92602-0_38
Attention-Guided Framework for Enhanced Brain Tumor Classification from MRI Images
Moondra, J., & Panwar, A. (2025) · doi:10.38124/ijisrt/25aug1132
Comparative analysis of machine learning models for brain tumor detection: a meta-analysis
Huang, T., Yin, X., & Jiang, E. (2025) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-025-01033-5
“Cheng et al. (Cheng et al. 2015) employed a Support Vector Machine (SVM) scheme to classify brain tumors, achieving an accuracy of 91.28%.”
InfluentialUniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
Hu, Y., Chang, S., Zhang, L., Tian, F., Sun, W., & Lu, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04937-7_63
BRAIN TUMOR SEGMENTATION VIA MULTI-SCALE ATTENTION GUIDANCE AND DYNAMIC WEIGHT OPTIMIZATION
Wu, F., YANG, C., & LI, C. (2025) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519425400871
Nath, P., & Trivedi, N. K. (2025) · International Conference on Next Generation Computing Systems (ICNGCS) · doi:10.1109/icngcs64900.2025.11183457
“To eliminate these drawbacks, scholars have gone to great length in order to investigate methods for totally and partially automated brain tumor segmentation [6].”
A Study on the Performance Comparison of Brain MRI Image-Based Abnormality Classification Models
Jeong, J.-H., Bang, S., Jung, Y., & Jo, J.-H. (2025) · Life · doi:10.3390/life15101614
“Research focusing on performance enhancement is pursuing high sensitivity and specificity by introducing data augmentation and transfer learning, or by fine-tuning model architecture and hyperparameters [7,8].”
Mirindi, D., Mirindi, F., & Khang, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36434-1.00037-9
Task-Specific Architecture Analysis for Brain Tumor Classification
SONAR, E. N., & Çakır, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-08384-5_27
A Deep Learning Techniques for Brain Tumor Detection Using MRI Imaging : A Review
Joshi, P. V. M., Sudhakar, T. G., & Dattatray, H. P. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-29643
Classification of Brain MRI Images Using Deep Learning Networks
Madhavan, R., Christ, M. C. J., Haripriya, R., Al-Milli, N., Mythily, V., Sajid, P. M., & Giri, J. P. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-89771-9_125
Chole, V., Moolchandani, J., Verma, S., Satone, S., & Thawakar, M. (2025) · Journal of Neuro-Oncology · doi:10.1007/s11060-025-05311-7
Brain Tumor Segmentation in MRI Images Using U-Net
Bhatt, N., Prajapati, P., Bhatt, N., & Bhalavat, J. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06697-8_43
An Efficient CNN-Based Approach for Binary Classification of Brain Tumors
Rai, N., & Sakalle, V. (2025) · International Conference on Next Generation of Green Information and Emerging Technologies (GIET) · doi:10.1109/giet65294.2025.11234827
“In [46], 233 individuals with brain malignancies were classified using SVM and KNN. 91.28% was the accuracy score.”
InfluentialBrain Disease Detection Using Image Processing and Machine Learning: A Review
Rai, N., & Sakalle, V. (2025) · International Conference on Next Generation of Green Information and Emerging Technologies (GIET) · doi:10.1109/giet65294.2025.11234824
“The accuracy score for this study was 91.28 percent [46].”
InfluentialAttentional Convolutional Architecture for Brain Tumor Recognition
Tarek, S., & Hamdi, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6441-2_36
Fuzzy-EDTrans: A Fuzzy Ensemble Learning Approach for Brain Tumor Classification
Phan, A.-C., Ho, C.-K., & Nguyen, K.-T. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-3358-9_14
Multi-Task Learning Framework for Brain Tumor Segmentation and Classification
Zaman, Z., Chowdhury, A. T., Tanmoy, T. N., Ahmed, N., & Mridha, A. I. (2025) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai68534.2025.11318534
“Cheng et al. [8] introduced a classification approach us-ing augmented regions of interest with bag-of-words encoding and SVM, achieving 91.28% accuracy but facing limitations due to data scarcity.”
InfluentialAresta, S., Palmirotta, C., Asim, M., Battista, P., Cava, C., Fiore, P., Santamato, A., Vitali, P., Castiglioni, I., D'Anna, G., et al. (2026) · medRxiv · doi:10.64898/2026.01.13.26344038
Brain Tumor Progression Forecasting via Multi-Modal Deep Learning
Joshi, S., Mirthika, G. L. S., Krithika, T., & Balaji, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6400-1.ch008
Hou, X., Li, N., Yu, J., Hao, Y., Ma, C., Pan, J., Liu, J., & Zhang, P. (2026) · Annals of Thoracic Medicine · doi:10.4103/atm.atm_207_24
Enhancing Brain Tumor Classification Through Image Subtraction Analysis
Mishra, M. R., Nayak, J., Tripathy, A. A., Jena, S., Bhui, M. R., & Meher, P. K. (2025) · International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) · doi:10.1109/ic-cgu67042.2025.11338093
A Deep Convolutional Framework for the Classification of Brain Tumor Images
Choudhury, K. R., Pal, A., Kumar, A., Sinhababu, D., Sharma, Y., & Chel, H. (2025) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip68302.2025.11346182
“To address this issue, the application of machine learning (ML) [22, 6, 8, 3, 19] and deep learning (DL) techniques [23, 21, 2, 6, 9, 1] for automated analysis of MRI images has gained significant attention.”
Veljanoski, D., Golbaf, A., Chawda, P., Thurston, M., Cutajar, J., Hilton, D. A., Teo, M., Werndle, M. C., Sahm, F., Gaudl, S. E., et al. (2026) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdag017
Acheampong, E. M., Zhou, S., Yongjian, L., Antwi‐Boasiako, E., Agbley, B. L. Y., & Ayekai, B. J. (2025) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip68645.2025.11352603
Multi-Model Deep Learning Framework for Brain Tumor Classification Using MRI and Genomic Data
Jayesh, S., & Sophia, S. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362753
Optimizing MRI-Based Brain Tumor Classification: A Study on Model Efficiency and Accuracy
Pallav, P., & Keshri, A. K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-14908-4_26
Gastrointestinal image classification with GIDNet CNN model and non-linear Tansh activation function
Mondal, A., Chatterjee, A., & Reigler, M. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111500
Automated Brain Tumor Segmentation and Classification Using Enhanced MRI Reconstruction via SwinMR
Sharma, D., Pal, R., & Kinjal (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15407-1_19
Contrast-enhanced MRI segmentation and classification with optimized vector machine classifier
Asiri, A. A. M., Soomro, T. A., Ubaid, F. B., Jandan, F. A., Irfan, M., Mehdar, K. M., Alelyani, M., Alshuhri, M. S., Alghamdi, A., & Alamri, S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3482
Brain Tumor Detection, Segmentation, and Classification Using Computer Vision Techniques
Yin, N. S., & Ponnusamy, R. R. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-7241-7_46
Channel Attention-Enhanced MobileNet with PCA-SVM for Brain Tumor Image Analysis
Shivakumar., E., & Jayaprakash, M. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11377598
“An innovative methodology for diagnosing brain cancers in T1-weighted contrast-enhanced MRI (CE-MRI) images using tumor region augmentation [4] and segmentation was proposed by Cheng, Huang, Cao, Yang, Yang, Yun, Wang, and Feng (2015).”
Vishwakarma, S., Kathane, M., Nakrani, J., Nair, D., & Lakhe, A. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11378629
“[ 22] The deployment illustrates that this solution is ready to start being implemented along clinical pathways, and certainly has the ability to be further scaled up and utilized into contexts where AI can be incorporated into a high-throughput diagnostic process.”
A Lightweight Convolutional Neural Network with Symbolic Reasoning for Brain Tumor Diagnosis
Akshita, K. C., Suchet, I. M., Giridhar, S., Durga, P. D., Siddartha, K. S., & Poola, R. G. (2025) · IEEE International Conference for Women in Computing (InCoWoCo) · doi:10.1109/incowoco68239.2025.11406939
“Pereira et al. [10] and Cheng et al. [11] focused on accurate segmentation and tumor region augmentation strategies to boost CNN performance on volumetric data.”
3D Self-Supervised Pre-Training and 3D-to-2D Transfer Learning for Brain Tumor MRI Classification
Halder, K. K., & Babli, I. A. (2026) · International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) · doi:10.1109/icecte69292.2026.11429246
“• Cheng/Figshare Brain Tumor Dataset [8]: We adhere to the provided 5-fold split indices (cvind) and report performance aggregated across all folds (mean ± std).”
Metadata
- Authors
- Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q.
- Affiliations
- Southern Medical University (CN)
- Nanfang Hospital (CN)
- Corresponding author
- Feng, Qianjin
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-08
- Volume
- 10
- Issue
- 10
- Pages
- e0140381
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4598126
- OpenAlex
- W2182098131
- Cited by
- 879
- References
- 36
- Citations by year
- 2026: 110 · 2025: 134 · 2024: 142 · 2023: 152 · 2022: 128 · 2021: 85 · 2020: 63 · 2019: 44 · 2018: 14 · 2017: 4 · 2016: 2 · 2015: 1
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Update notices
- correction (2015-12-02)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (81101109, 31371009, 2012BAI14B02, 863 Program)
- National High-tech Research and Development Program
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor classification, meningioma, glioma, pituitary tumors, region of interest, image dilation, spatial pyramid matching, contrast-enhanced T1-weighted imaging, computer-aided diagnosis, gray level co-occurrence matrix, bag-of-words model, intensity histogram
- MeSH terms
- Brain Neoplasms, Glioma, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Meningioma, Sensitivity and Specificity
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