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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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Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 1×
“(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
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464
Robust Face Recognition via 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×
“The BoW method was originally used in the text retrieval domain, and it has been successfully adapted to the visual analysis domain [13,14].”
MethodRobust wide-baseline stereo from maximally stable extremal regions
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
Fei-Fei, L., & Perona, P. (2005) · 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.2005.16 · Cited by 3,665 · cited 1×
“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×
“Other more sophisticated methods may also be used, such as SC, soft assignment (SA) [24,25], orthogonal matching pursuit (OMP), locality-constrained linear coding [26], and local anchor embedding [27].”
MethodA comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
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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Items 501–600 of 879
Brain Tumor Classification using Region-based CNN with Chicken Swarm Optimization
Peddinti, A. S., Maloji, S., & Mannepalli, K. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i5.3162 · Cited by 4
“Meningiomas, gliomas, and pituitary gland tumors are all in this group [23].”
BackgroundHuang, Z., Yang, Q., Tian, M., & Gao, Y. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70007 · Cited by 4
Sinha, T., & Verma, B. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308203 · Cited by 4
“…using Softmax (in By analysing the results of TABLE VI, it was found that the overall test accuracy obtained by the proposed ConFED method, was higher than the traditional machine learning methods [13] [14] [29] and has also outperformed recently published methods [31] [32] [33] using same dataset.”
BackgroundMethodA Clustering Based Approach For Meningioma Tumors Extraction From Brain MRI Images
Sahoo, A. K., & Parida, P. (2020) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc50941.2020.9358849 · Cited by 4
“The proposed method is compared with various state of art approaches such that [5], [13], [16]–[20] using the performance metrics discussed in former paragraph.”
MethodThe Effect of Deep Learning and Machine Learning Approaches for Brain Tumor Recognition
Arumaiththurai, T., & Mayurathan, B. (2021) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs52090.2021.9605909 · Cited by 4
Medial Residual Encoder Layers for Classification of Brain Tumors in Magnetic Resonance Images
Sobhaninia, Z., Karimi, N., Khadivi, P., & Samavi, S. (2022) · doi:10.1109/icee55646.2022.9827434 · Cited by 4
Hybrid Texture-Based Feature Extraction Model for Brain Tumour Classification Using Machine Learning
Rather, I. H., Minz, S., & Kumar, S. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-4676-9_38 · Cited by 4
A Comparative Analysis of Wavelet Transforms for Denoising MRI Brain Images
Sonia, M. S., & Sumathi, S. (2022) · International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) · doi:10.1109/icpects56089.2022.10046675 · Cited by 4
“Because noise restricts the precision of medical image diagnosis, it is crucial to remove noise as well as disturbance from the images [2, 6].”
BackgroundBrain tumor classification based on deep CNN and modified butterfly optimization algorithm
Jacob, V., Sagar, G. V. R., Goura, K., & Pedalanka, P. S. S. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2219754 · Cited by 4
“In detection (Thales et al. 2018; Diniz et al. 2021), deep learning techniques (Cheng et al. 2015; Diniz et al. 2020; Menaga and Revathi 2021) have gained much traction.”
BackgroundRelevant edge probability‐based adaptively weighted active contour for medical image segmentation
Kumar, B., Panda, R., & Agrawal, S. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22993 · Cited by 4
Almukhtar, F. H., Kareem, S. W., & Khoshaba, F. S. (2024) · Egyptian Informatics Journal · doi:10.1016/j.eij.2024.100454 · Cited by 4
Brain Tumor Classification Using Convolutional Neural Networks and Deep Learning
Csaholczi, S., Kovács, L., & Szilágyi, L. S. (2024) · IEEE International Conference on Computational Cybernetics (ICCC) · doi:10.1109/iccc62278.2024.10582939 · Cited by 4
“…vs. low-grade tumor etc.), based classification applied to features extracted by CNN [20], recurrent CNN [21], long short-term memory (LSTM) network 23], EfficientNet and multiple path convolution [24], and parallel deep CNN [25]. architecture built from scratch and optimize it to perform well in…”
BackgroundJehangir, B., & Nayak, S. R. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7076-3_27 · Cited by 4
Dual Learning Model for Multiclass Brain Tumor Classification
Thanki, R. M., & Kaddoura, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-06746-4_34 · Cited by 4
Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model
Arshed, M. A., Shahzad, A., Arshad, K., Karim, D., Mumtaz, S., & Tanveer, M. (2022) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v10i4.1182 · Cited by 4
Automatic CAD System for Brain Diseases Classification Using CNN-LSTM Model
Gore, D. V., Sinha, A. K., & Deshpande, V. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-4676-9_54 · Cited by 5
Lesion Localization and Extreme Gradient Boosting Characterization with Brain Tumor MRI Images
Raja, P. M. S., & Ramanan, K. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-0978-0_39 · Cited by 4
Brain Tumor classification using BrainNet: A Deep Learning ApproachPreprint
Wagh, A., Bhosale, A., Singh, T., Nair, R. R., & Babu, T. (2023) · Research Square · doi:10.21203/rs.3.rs-2502279/v1 · Cited by 4
A Light-Weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation
Yang, S., Li, Y., Chen, Y., Wu, Z., & Okutomi, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81854-7_6 · Cited by 3
“…Abdominal Endoscopy PAPILA [36] Eye Fundus IDRiD [52] Eye Fundus NeurIPS CellSeg [44] Cells Microscopy Aorta CT Figshare Brain Tumor Dataset [11] Brain MR Uwaterloo skin cancer [21] Skin Dermoscopy BKAI-IGH NeoPolyp [5] Abdominal Endoscopy CT2USforKidneySeg [59] Kidney US Ultrasound Nerve…”
BackgroundGhodhbani, H., Rimer, S., Ouahada, K., & Alimi, A. M. (2025) · IEEE Access · doi:10.1109/access.2025.3552593 · Cited by 4
KANBalance: Kolmogorov–Arnold network mitigates class imbalance
Qezelbash-Chamak, J., Hicklin, K., & Kim, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112325 · Cited by 3
Sundararajan, P., Dharmaraj, S., & Gadiraju, P. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00635-w · Cited by 4
“Our comprehensive dataset comprised 7,523 MRI brain images collected from three established repositories: Fig - share, SARTAJ, and Br35H (Cheng et al. 2015).”
Enhancing brain tumor diagnosis: the role of topological features in deep learning approaches
Joshi, D., Singh, B. K., Nagwanshi, K. K., & Choubey, N. S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109072 · Cited by 3
Ultra-lightweight uncertainty-aware ensemble for large-scale multi-class medical MRI diagnosis
Rahman, S., Farid, F. A., Zabin, M., Uddin, J., & Karim, H. A. (2025) · Frontiers in Radiology · doi:10.3389/fradi.2025.1723272 · Cited by 3
Brain tumor classification model guided by class activation mapping
Ma, Y., Zhang, W., Feng, Y., Peng, M., Tang, S., Zhu, Y., Xiong, H., & Chen, S. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102726 · Cited by 3
Saifullah, S., Dreżewski, R., Yudhana, A., Tanone, R., & Suryotomo, A. P. (2026) · Applied Sciences · doi:10.3390/app16063041 · Cited by 3
Online Brain Image Repositories for Brain Disease Detection
(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.b1134.1292s219 · Cited by 3
Fareed, M. J., Pluempitiwiriyawej, C., & Murtaza, G. (2024) · IEEE Karachi Section Humanitarian Technology Conference (KHI-HTC) · doi:10.1109/khi-htc60760.2024.10482091 · Cited by 3
“It was discovered that performance on the classification test using the Cheng et al. [7] dataset did not notably improve solely by incorporating the original CBAM into a rapid baseline model.”
BackgroundMethodInfluentialBALCI, F. (2024) · Traitement du signal · doi:10.18280/ts.410217 · Cited by 3
“First, the dataset called Dataset A is the dataset publicly shared by Cheng et al. [45].”
Dual-branch Brain Tumor Classification Based on CNN and Transformer
Wang, Q., Shi, H., Wu, X., & Li, X. (2024) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict61637.2024.10670751 · Cited by 3
“We evaluated the performance of our proposed model using the CE-MRI brain tumor dataset [23]”
MethodBrain MRI Tumour Localization and Segmentation Through Deep Learning
Davar, S., & Fevens, T. (2024) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas60917.2024.10658897 · Cited by 3
“Evaluations were conducted comparing the proposed method with state-of-the-art approaches as outlined in [22], [24], [28].”
MethodApplications of Neural Networks in Brain Tumors Detection and Classification: A Review
Elmejgari, C., Nadir, Y., & Qbadou, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70411-6_38 · Cited by 3
Chowdhury, P., & Srivastava, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78104-9_1 · Cited by 3
Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edge Detection and Deep Learning
Vu, H. A., & Vajda, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78201-5_15 · Cited by 3
Rastogi, D., Singh, A. M., Dhanda, S. S., Bindewari, S., & Yadav, S. A. (2024) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i61595.2024.10829336 · Cited by 3
MLG: a mixed local and global model for brain tumor classification
Chen, W., Tan, X., Zhang, J., Du, G., Fu, Q., & Jiang, H. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1618514 · Cited by 3
“The Chen dataset, provided by Cheng et al. (2015), primarily focuses on three types of brain tumors: gliomas, meningiomas, and pituitary tumors.”
Brain Tumour Diagnostics and Analysis (TDA): Segmentation, Classification and Interactive Interface
Farhan, A. S., Manzoor, U., & Khalid, M. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00100 · Cited by 3
“• Dataset A: the brain tumour dataset introduced by [18], [19] is used to train/test the PRCnet model for tumour”
Mubarok, A. F. A., Thias, A. H., Handayani, A., Danudirdjo, D., & Rajab, T. L. E. (2019) · International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) · doi:10.1109/morse48060.2019.8998672 · Cited by 3
“Another brain tumor classification study has shown that Bag of Visual Words (BoVWs) can be used as features extractor which provides a vector of visual word occurrences based on clustering centroids of its feature distributions [4].”
BackgroundResultAnjum, S., Hussain, L., Ali, M., & Abbasi, A. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66843-3_24 · Cited by 3
“The CNN was trained on similar dataset consisting of 3064 T-1 weighted CE-MRI images publicly available Cheng et al. [22] yielded a training accuracy of 98.51% and validation accuracy of 84.19% at best.”
BackgroundMethodInfluentialA Hybrid Deep Model for Brain Tumor Classification
Ayaz, H., Ahmad, M. I., Tormey, D., McLoughlin, I., & Unnikrishnan, S. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3880-0_29 · Cited by 3
Asthana, P., Hanmandlu, M., & Vashisth, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12482-2 · Cited by 3
“Dataset from [16, 17] The performance measures used for judging the proposed algorithm include: Accuracy, Sensitivity, Specificity, False positive rate, False negative rate, Error rate, F1-score, and Precision.”
BackgroundMethodAn Approach for Enhancement of MR Images of Brain Tumor
Mishra, H. K., & Kaur, M. (2022) · Traitement du signal · doi:10.18280/ts.390405 · Cited by 3
Patil, D., & Hamde, S. T. (2022) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-022-00557-7 · Cited by 3
“The proposed brain tumor classification algorithm is evaluated using the database provided by Cheng et al. (2015”
MethodA Deep Feature Ensemble-Based Hybrid Model for MRI Brain Tumor Multi-Classification
Munira, H. A., & Islam, M. S. (2022) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti56238.2022.10103342 · Cited by 3
“Because brain tumors differ greatly in size and shape, subtyping them is a challenging problem [2].”
BackgroundLOBO Optimization-Tuned Deep-Convolutional Neural Network for Brain Tumor Classification Approach
Nisha, A. S. A., Narmadha, R., Amirthalakshmi, T. M., Balamurugan, V., & Vedanarayanan, V. (2023) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-023-2625-8 · Cited by 3
The Performance of Transferability Metrics Does Not Translate to Medical Tasks
Chaves, L. G., Bissoto, A. E., Junior, E. A. D. V., & Avila, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45857-6_11 · Cited by 3
A CNN model with activation to classify MRI images for brain tumor
Mishra, B., Gopal, K. M., Patnaik, S., Paikaray, B. K., & Pramanik, J. (2023) · AIP conference proceedings · doi:10.1063/5.0182675 · Cited by 3
Yamini, K., Reddy, K. N. K., & Jaswanth, T. (2023) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna58489.2023.10370523 · Cited by 3
“[10]It contains rely on nerve tissues and cells to control the body's fundamental processes, such as breathing, physical movement, and senses.”
BackgroundMRI Image Segmentation: Brain Tumor Detection and Classification Using Machine Learning
Mahajan, S., Sahoo, A. K., Sarangi, P. K., Rani, L., & Singh, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6547-2_11 · Cited by 3
Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-Net Cascade Architecture
Dénes-Fazakas, L., Kovács, L., Eigner, G., & Szilágyi, L. S. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394588 · Cited by 3
“The brain tumor data set involved in the training and evaluation of the modified VGG model proposed in this study was published by Cheng et al. [26], [27].”
MethodBrain Tumor Detection Using GLCM and Machine learning Techniques
ibrahim, Y. T., Elgohary, R., & Deif, M. A. (2024) · International Integrated Intelligent Systems · doi:10.21608/iiis.2024.357817 · Cited by 3
“Cheng et al[13]. presented a system for brain tumor analysis utilizing a dataset where the tumor area was delineated and subdivided into sub-areas using an adaptive spatial algorithm.”
MethodDudeja, T., Dubey, S. K., & Bhatt, A. K. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02402-y · Cited by 3
A NOVEL APPROACH FOR CLASSIFICATION OF BRAIN TUMOR USING R-CNN
Murali, E., & Meena, K. (2019) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2019.v04i04.058 · Cited by 3
A Comparative Study of Meningioma Tumors Segmentation Methods from MR Images
Alkhodari, M., Hassanin, O., & Dhou, S. (2020) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis51252.2020.9340134 · Cited by 3
“Although diagnosing such tumors accurately helps in improving the overall treatment process, medical diagnosis is considered a major challenge for clinicians [2], [3].”
BackgroundClassifying Brain Tumor from MRI Images Using Parallel CNN Model
Sumi, T. A., Nath, T., Nahar, N., Hossain, M. S., & Andersson, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15037-1_22 · Cited by 3
Zia, R., Akhtar, P., Aziz, A., Shah, M. A., & Kundi, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63004-5_23 · Cited by 3
Smart Health App for Identifying Brain Tumors
Madapatha, W. E., Gunasekara, S. V. S., & Kumarage, P. M. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126477 · Cited by 3
“28% accuracy using an intensity histogram, a gray-level co-occurrence matrix, and a bag-ofwords model [10].”
MethodEl-Wahab, B. S. A., NASR, M. E. M., Khamis, S., & Ashour, A. S. (2023) · Journal of Engineering Research - Egypt/Journal of Engineering Research · doi:10.21608/erjeng.2023.217931.1188 · Cited by 3
“The task of classifying brain tumors into subtypes presents significant challenges due to a couple of factors: i) brain tumors exhibit considerable variations in intensity, and shape [7] and ii) tumors from different uncontrolled types may have comparable presence [8].”
BackgroundAnnadurai, A., Joseph, B., & Prusty, M. R. (2023) · Research Square · doi:10.21203/rs.3.rs-3264388/v1 · Cited by 3
Barbhuiya, R. K., & Paul, C. (2025) · doi:10.1002/9781394245369.ch5 · Cited by 3
Dhar, J., Rana, K., & Goyal, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78398-2_21 · Cited by 2
Brain Tumor Detection Based on MRI Images and Artificial Intelligence
Li, Q. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.23007 · Cited by 2
Van, M.-H., Carey, A. N., & Wu, X. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00834-8 · Cited by 2
DEQ-KAN: Deep equilibrium Kolmogorov–Arnold networks for robust classification
Qezelbash-Chamak, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108087 · Cited by 2
Unified Medical Lesion Segmentation via Self-referring Indicator
Chang, S., Zhao, X., Zhang, L., & Wang, T. (2025) · 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/cvpr52734.2025.00974 · Cited by 2
“BTD [10, 11] divides the dataset into four folds for cross-validation.”
Multi‐class brain tumor diagnosis using MRI: A dynamic reinforcement ensemble learning approach
Yang, J. G., Xiao, Y., & Xu, X. S. (2025) · Medical Physics · doi:10.1002/mp.18003 · Cited by 2
“Cheng et al., 19 for example, utilized a tumor region enhancement strategy combined with features such as bag-of-words (BoW), intensity histograms, and gray level co-occurrence matrix (GLCM) to boost SVM performance.”
An Explainable Brain Tumor Classification System based on Fuzzy Decision Trees
Ducange, P., Fazzolari, M., Marcelloni, F., Miglionico, G. C., & Ruffini, F. (2025) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz62266.2025.11152123 · Cited by 2
“Originally introduced by [14] and later inspected in detail by [19] and [20], the Brain Tumor Public Data Set comprises T1-weighted and contrast-enhanced MRI scans from 233 patients treated at two Chinese hospitals between 2005 and 2010.”
Sina, E. M., Limage, K., Anisman, E. J., Pudik, N., Tam, E., Kahn, C., Daggumati, S., Evans, J. J., Rabinowitz, M., Rosen, M. R., et al. (2025) · Otolaryngology · doi:10.1002/ohn.70034 · Cited by 2
“16 Collectively, these fi ndings support our hypothesis that an AutoML ‐ based approach can achieve diagnostic accuracy equal to or, in select cases, exceeding that of conventional ML and DL models.”
InfluentialImproved SVM-Based Classifier Model for Brain Tumor Early Detection in the Modern Healthcare System
Das, D., Giri, P. K., & Mallick, C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8639-1_2 · Cited by 2
Classification of brain tumors from MRI images using deep transfer learning
Kazemi, A. (2023) · Journal of High School Science · doi:10.64336/001c.89930 · Cited by 2
Brain Tumor Detection Using Transformer Based EfficientB0 Net
Sahoo, J. R., Nanda, S. K., & Panda, G. (2026) · SN Computer Science · doi:10.1007/s42979-025-04550-9 · Cited by 2
Govil, N., Patro, S. G. K., Dash, A., Al-Nussairi, A. K. J., Hadi, A. A., Amir, M., & Sherzod, S. (2026) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2026.2626127 · Cited by 2
Cutting Edge Deep Learning Models for Brain Tumor Classification
Özger, F., Paçal, İ., & Sökmen, D. (2026) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.1684696 · Cited by 2
Saifullah, S., & Dreżewski, R. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01812-1 · Cited by 4
“The primary dataset is the Figshare Brain Tumor Segmentation (FBTS) dataset [57], containing 3064 brain MRI scans at a resolution of 512 × 512 pixels with a 24-bit color depth.”
Eun, S., Jee, S. H., & Kim, Y. (2026) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-026-01029-7 · Cited by 2
“All models showed comparable detection performance, but the recent version 11 showed a trend of decreasing [ 21].”
Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI
Uthra, B., & Sivashankari, R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1849051 · Cited by 2
Multi-class Glioma Classification from MRI Images Using 3D Convolutional Neural Networks
Sahayam, S., Jayaraman, U., & Teja, B. C. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1086-8_29 · Cited by 2
Fusing AutoML Models: A Case Study in Medical Image Classification
Dale, M. R., Ross, A. A., & Shapiro, E. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09282-4_15 · Cited by 2
Development of the Tumor Diagnosis Application for Medical Practitioners using Transfer Learning
Sarwar, N., Noreen, I., & Irshad, A. (2022) · BioScientific Review · doi:10.32350/bsr.42.05 · Cited by 2
Brain Tumor Classification Based on GAN and Semi-supervised Approach
Viet, K. L. D., Quoc, T. N., & Hoang, V. T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27524-1_42 · Cited by 2
Brain Tumor Diagnosis Using CNN
Amritraj, S., Mahendra, J., & Cyril, C. P. D. (2023) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect57570.2023.10117616 · Cited by 2
“Instead of using the initial tumor territory, which is the way ROIs are usually established, the authors in [11] chose to utilize the enlarged tumor region.”
BackgroundMedical image categorization combining image segmentation and machine learning
Altarawneh, K. (2023) · Journal of Namibian Studies History Politics Culture · doi:10.59670/jns.v33i.728 · Cited by 2
An Automated Abnormality Diagnosis and Classi?cation in Brain MRI using Deep Learning
Singh, R., & Agarwal, B. B. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i4.6446 · Cited by 3
Enhancement of medical images diagnosis using fuzzy convolutional neural network
Mahdi, H. A., Shujaa, M. I., & Zghair, E. M. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i6.5729 · Cited by 2
A Review on Machine Learning and Deep Learning Based Systems for the Diagnosis of Brain Cancer
Saha, P., Das, S. K., & Das, R. (2023) · SN Computer Science · doi:10.1007/s42979-023-02360-5 · Cited by 2
“Among 14 ML based systems, 7 systems [62, 66, 68, 69, 73, 74, 78] used SVM model and 3 systems [78, 79, 83] used K-means clustering.”
BackgroundMethodInfluentialSynthesizing with Diffusion model for improving medical image segmentation performance
Huang, Z., Yang, Q., Tian, M., & Gao, Y. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385456 · Cited by 2
“To conduct a comprehensive evaluation, we employ two publicly available datasets: MR images-based brain dataset (BMRI) [11] and US images-based breast dataset (BUSI) [12].”
MethodEffective Use of Clustering Techniques for Brain Tumor Segmentation
Sahoo, A. K., Parida, P., & Muralibabu, K. (2023) · IEEE International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC) · doi:10.1109/aespc59761.2023.10390467 · Cited by 2
Deep Learning Based Lightweight Model for Brain Tumor Classification and Segmentation
Andleeb, I., Hussain, B., Ansari, S., Ansari, M. S., Kanwal, N., & Aslam, A. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-47508-5_38 · Cited by 2
Automated Diagnosis of brain tumor classification and segmentation of MRI Images
Shanthala, K. V., Chandrakala, B. M., Shobha, N., & Deepashree, D. (2023) · International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) · doi:10.1109/ic-rvitm60032.2023.10435084 · Cited by 2
Hasan, A., Toufiq, R., & Islam, M. Z. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441068 · Cited by 2
“Numerous studies have already delved into the categorization and segmentation of brain MRI images, with one innovative method enhancing classification by augmenting tumor regions through image dilation and subdividing them into intricate fine-ring subregions [1].”
MethodAutomated Multi-Class Brain Tumor Classification for MRI Image Using Transfer Learning
Hasan, A., Toufiq, R., & Islam, M. Z. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441355 · Cited by 2
“To enhance the classification performance, a method uses augmented tumor region via image dilation and splits this region into increasing the fine-ring form subregions [1].”
MethodDeep Learning-based Neural Network for Automated Brain Tumor Diagnosis from MRI Images
Uthradevi, G., Mohanbabu, G., Kumar, B. R. S., Tamilselvi, A., Ashok, A., & Sangeetha, R. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452581 · Cited by 2
“Understanding these types of tumors necessitates diving into the complexity of their classification and origin, which have a substantial impact on diagnostic and treatment options [5].”
BackgroundInvestigating the Use of Gan in the Classification of Brain Tumors
Ah-Shee-Tee, M. J., Chikhuri, R., & Mungloo-Dilmohamud, Z. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487701 · Cited by 2
Brain Tumor Classification using Transfer Learning and Ensemble Approach
Shaikh, J., & Shaikh, K. (2024) · Journal of Soft Computing Paradigm · doi:10.36548/jscp.2024.3.005 · Cited by 2
Enhancing Brain Tumor Classification and Segmentation Using ResNet50
Dash, Y., Anand, P., Deepali, D., Chaudhary, P., Sarangi, S. C., & Abraham, A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724031 · Cited by 2
“Applying these trained models to the majority voting method of brain tumour classification made efficient use of the strengths of each of the five CNN models, thanks to the 3064 T1W-CE MRI datasets [20].”
MethodTasnim, T., & Parvin, F. (2024) · IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON) · doi:10.1109/peeiacon63629.2024.10800011 · Cited by 2
“Jun Cheng et al. [10] developed an approach to improve models’s accuracy.”
Brain Tumor Classification Using Deep Learning with Explainable AI (XAI)
Asif, I. H., Haque, S. B., Nawaz, M. F., Rahman, M. M., & Khan, M. M. (2025) · International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) · doi:10.1109/iementech65115.2025.10959473 · Cited by 2
“Deep learning, a subset of artificial intelligence (AI) has emerged as a powerful tool for image classification [2-3].”
Revolutionizing Brain Tumor Detection: Unleashing the Power of Artificial Intelligence
Shinde, N., Patil, M., Sharma, R. K., Kadam, S., & Kashte, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-4241-0_16 · Cited by 2
CNN and RF Based Classification of Brain Tumors in MR Neurological Images
Saraswathi, V., Jamthikar, A. D., & Gupta, D. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4015-8_11 · Cited by 2
Segmentation Based on Propagation of Dynamically Changing Superpixels
Данилов, В., Gerget, O. M., Skirnevskiy, I. P., Manakov, R. A., & Kolpashchikov, D. Y. (2020) · Programming and Computer Software · doi:10.1134/s0361768820030044 · Cited by 2
“To estimate the performance of the algorithms, we used data collected at the University of York (York, UK) [26] and Southern Medical University (Guang-zhou, China) [27, 28].”
Method
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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