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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
Distinctive Image Features from Scale-Invariant Keypoints
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
Wright, J. N., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,398 · cited 1×
“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 401–500 of 879
Dénes-Fazakas, L., Kovács, L., Eigner, G., & Szilágyi, L. S. (2024) · Biomedicines · doi:10.3390/biomedicines12102388 · Cited by 9
“The dataset used for training and evaluating all CNN models involve in this study were originally compiled and made available for the public by Cheng et al. [19,20].”
MethodSb-PiPLU: A Novel Parametric Activation Function for Deep Learning
Mondal, A., Shrivastava, V. K., Chatterjee, A., & Ramachandra, R. (2022) · IEEE Access · doi:10.1109/access.2025.3561464 · Cited by 8
“Brain Tumor [37]: A dataset of 3,064 grayscale images of size (512 × 512), categorized into three types of brain tumors: meningioma, glioma, and pituitary tumor.”
Ali, M. U., Kallu, K. D., Masood, H., Hussain, S. J., Ullah, S., Byun, J. H., Zafar, A., & Kim, K. S. (2022) · Life · doi:10.3390/life12122036 · Cited by 9
“In a previous study [24], the authors used the histogram intensity, the bag-of-words model, and the gray-level co-occurrence matrix to identify MRI brain images.”
MethodResultA Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans
Bodapati, J. D., Ahmed, S. F., Chowdary, Y. Y., & Sekhar, K. R. (2023) · Information Dynamics and Applications · doi:10.56578/ida020105 · Cited by 9
Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks
Montoya, S., Rojas, A. E., & Vásquez, L. F. N. (2024) · SN Computer Science · doi:10.1007/s42979-023-02431-7 · Cited by 9
“The first study [6] provides a vital foundation, as it introduces the shared dataset and employs it for analysis, focusing on the automatic classification of tissue types within the region of interest (ROI) in T1-weighted contrast-enhanced MRI (CE-MRI) images.”
Ingle, A., Roja, M. M., Sankhe, M. S., & Patkar, D. (2022) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.13.8.4 · Cited by 11
“The dataset used is publicly available at figshare. com and Kaggle.com, which was presented by Cheng et al. [18].”
MethodClassification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net
Adinegoro, A. F., Sutapa, G. N., Gunawan, A. A. N., Anggarani, N. K. N., Suardana, P., & Kasmawan, I. G. A. (2023) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2023/v15i3320 · Cited by 10
Two phase cooperative learning for supervised dimensionality reduction
Nellas, I. A., Tasoulis, S. K., Georgakopoulos, S. V., & Plagianakos, V. P. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109871 · Cited by 9
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Bechar, A., Elmir, Y., Medjoudj, R., Himeur, Y., & Amira, A. (2023) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis60075.2023.10344192 · Cited by 9
“This data was used in [29] and [30].”
MethodDeep Learning with EfficientNetB1 for detecting brain tumors in MRI images
Benkrama, S., & Hemdani, N. E. H. (2023) · International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) · doi:10.1109/icaeccs56710.2023.10104761 · Cited by 8
“The model was 91.28% accurate on a dataset of 3064 MR images of the brain [8].”
MethodInfluentialKim, H., Kim, H., Oh, J.-H., & Lee, K. M. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-114 · Cited by 8
A computer-aided diagnosis system for brain tumors in magnetic resonance imaging (MRI)
Abdulla, A. A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20117-x · Cited by 8
Vijayalakshmi, B., & Anand, S. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110392 · Cited by 8
ODTL: An Optimal Deep Transfer Learning model for brain tumor classification
Mallouk, O., Joudar, N., & Ettaouil, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130747 · Cited by 9
Semi-analytical penalized threshold dynamics method for binary image segmentation
Su, S., & Yang, J. (2025) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2025.10.029 · Cited by 8
Classification of Brain Tumor on Magnetic Resonance Imaging Using Support Vector Machine
Hasanah, U., Sigit, R., & Harsono, T. (2021) · International Electronics Symposium (IES) · doi:10.1109/ies53407.2021.9594004 · Cited by 8
Abbas, A., Gaber, M. M., & Abdelsamea, M. M. (2022) · Sensors · doi:10.3390/s22249875 · Cited by 9
Brain tumor classification using DenseNet and U-net convolutional neural networks
Belaid, O. N., Loudini, M., & Nakib, A. (2024) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa59904.2024.10536704 · Cited by 8
Brain Tumor Segmentation of MRI Images with U-Net and DeepLabV3+
Akagić, A., Kapo, M., Kandić, E., Bećirović, M., & Kadrić, N. (2024) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi60790.2024.10585749 · Cited by 8
PRCnet: An efficient model for automatic detection of brain tumor in MRI images
Farhan, A. S., Khalid, M., & Manzoor, U. (2025) · PLoS ONE · doi:10.1371/journal.pone.0292768 · Cited by 9
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis
Zhou, Y., Huang, L., Zhou, T., & Shao, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00815 · Cited by 8
“BrainTumor [7] has 3,064 T1-weighted contrast-enhanced (T1-ce) images from 233 patients with three kinds of brain tumor: menin-”
BackgroundInvariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval
Putzu, L., Loddo, A., & Ruberto, C. D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89128-2_28 · Cited by 8
Sen, G., Wu, M., Liu, Q., & Yan, C. (2022) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405618666220415122843 · Cited by 9
Efficient algorithms for compression and classification of brain tumor images
Ghamry, F. M., Emara, H. M., Hagag, A., El‐Shafai, W., El‐Banby, G. M., Dessouky, M. I., El‐Fishawy, A. S., El‐Hag, N. A., & El‐Samie, F. E. A. (2023) · Journal of Optics · doi:10.1007/s12596-022-01040-6 · Cited by 8
An Optimized CNN Method for Multi-Class MRI Brain Tumor Classification
Kollem, S. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10275897 · Cited by 8
“The implementation of the proposed method is covered in the following part, which is organized as follows.”
MethodSun, Y.-C., Hsieh, A.-T., Fang, S.-T., Wu, H., Kao, L.-W., Chung, W., Chen, H., Liou, K.-D., Lin, Y., Guo, W., et al. (2021) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000000614 · Cited by 8
“Cheng et al 9 introduced a study focusing on the classification of three types of brain tumors (ie, meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MR images.”
BackgroundBrain Tumor Detection by using ResNet-50 and Image Processing Tools
Rathore, S., Rana, T., Mittal, U., Gupta, T., Malik, N., & S, M. S. A. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10183081 · Cited by 7
An Improved EfficientNet-B0 Approach for Brain Tumor MRI Image Classification
Li, X., Chen, X., He, L., Wu, Y., Du, L., & He, W. (2025) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec63606.2025.10968077 · Cited by 7
“Reference [1] employed three feature extraction methods, including gray-level histogram, gray-level co-occurrence matrix (GLCM), and Bag of Words (BOW) model, with traditional machine learning approaches to classify three types of brain tumors (meningioma, glioma, and pituitary tumor), achieving a…”
PM2: A new prompting multi-modal model paradigm for few-shot medical image classification
Wang, Z., Sun, Q., Zhang, B., Wang, P., Zhang, J., & Zhang, Q. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109042 · Cited by 9
Self-citationDas, A., & Singh, S. (2025) · Sensors · doi:10.3390/s25237341 · Cited by 7
“For example, in medical image classification, techniques like Affinity-Net demonstrated effective disease type prediction with semi-supervised FSL, while metric learning approaches, such as Siamese Networks and Triplet Networks, have been influential for image recovery and modality recognition [9].”
Ganie, S. M., Tanguturi, R. C., & Alfuraydan, M. M. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114128 · Cited by 7
Improved medical image inpainting using automatic multi-task learning driven deep learning approach
Rakibe, P. L., & Patil, P. D. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100678 · Cited by 7
Anjum, S., Hussain, L., Ali, M., Abbasi, A. A., & Duong, T. Q. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021146 · Cited by 7
Artificial Intelligence Based Brain Tumor Localization Using YOLOv5
Sadrawi, M., Fugaha, D. R., Heerlie, D. M., Lorell, J., Gautama, N. R. P., & Aminuddin, M. Z. (2023) · Indonesian Journal of Life Sciences · doi:10.54250/ijls.v5i01.176 · Cited by 7
Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning
Mowlani, K., Shahbazzadeh, M. J., & Hashemipour, M. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04754-7 · Cited by 7
Revealing Brain Tumor Using Cross-Validated NGBoost Classifier
Dutta, S., & Bandyopadhyay, P. S. K. (2020) · International Journal of Machine Learning and Networked Collaborative Engineering · doi:10.30991/ijmlnce.2020v04i01.002 · Cited by 11
Ismael, M. R. (2018) · ScholarWorks - WMU (Western Michigan University) · Cited by 7
Brain Tumor Classification Using Medial Residual Encoder LayersPreprint
Sobhaninia, Z., Karimi, N., Khadivi, P., Roshandel, R., & Samavi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00628 · Cited by 7
“Extracted features were given to some classifiers such as SVM, SRC, and KNN to perform the classification operation [8].”
MethodLawrence, M. O. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00207-y · Cited by 6
“For instance, in a max pooling operation of a pool size of P x P , the output is computed by taking the maximum value within each P x P patch of the input [66].”
Advanced brain tumor detection using YOLO- β 11 in MRI images
Raju, N. N., Srinivas, K., Rajesh, C., & Chintakindi, B. M. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.10.019 · Cited by 10
Ducange, P., Fazzolari, M., Marcelloni, F., Miglionico, G. C., & Ruffini, F. (2026) · Information Systems Frontiers · doi:10.1007/s10796-025-10683-2 · Cited by 7
Fast Convolutional Neural Network with iterative and non-iterative learning
Sinha, T., & Verma, B. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109197 · Cited by 6
Shaker, E. A., El‐Hossiny, A. S., Kandil, A. H., El-Bialy, A., & Afify, H. M. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22877 · Cited by 7
Classification of brain tumour based on texture and deep features of magnetic resonance images
Mishra, H. K., & Kaur, M. (2023) · Expert Systems · doi:10.1111/exsy.13294 · Cited by 6
“(Cheng et al., 2015) collected the data set from General Hospital, Nanfang Hospital, Guangzhou, Tianjin Medical University, Tianjin, China, from 2005 to 2010, which is freely available at https://figshare.”
MethodImage Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
Saranya, R., Pushpakumar, R., Srinivasan, P., Aparna, N., Karthika, K., & Chauhan, A. (2023) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs58579.2023.10405052 · Cited by 6
“[5]This method's research seeks to develop and test a Deep Learning Using a Multi scale Convolutional Neural Network for Tumor Classification and Segmentation in the Brain Interconnected Minds.”
MethodDeep Learning-Based Hyperparameter Tuning and Performance Comparison
Tan, F. G., Yüksel, A. S., & Aksoy, B. (2024) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-56310-2_11 · Cited by 6
Brain Tumor Segmentation Based on α‐Expansion Graph Cut
Soloh, R., Alabboud, H., Shahin, A. M., Yassine, A., & Chakik, A. E. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23132 · Cited by 6
“We encourage readers to refer to the main works [24, 25] for viewing the original images.”
Cao, Y., Guan, H., Qiu, W., Shen, L., Liu, H., Tian, L., Hou, D., & Zhang, G. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.125235 · Cited by 6
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification
Wang, Z., Sun, Q., Zhang, B., Su, W., Wang, P., Zhang, J., & Zhang, Q. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821863 · Cited by 7
“The Figshare MRI brain tumor dataset [6] consists of 3064 T1-weighted contrast-enhanced images from 233 patients, divided into three brain tumor categories: meningioma, glioma, and pituitary tumors, with respective image counts of 708, 1426, and 930.”
Self-citationImage Segmentation and Classification Using CNN Model to Detect Brain Tumors
Hilles, S. M. S., & Saleh, N. S. (2021) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec54230.2021.9672428 · Cited by 8
On The Potential of Image Moments for Medical Diagnosis
Ruberto, C. D., Loddo, A., & Putzu, L. (2023) · Journal of Imaging · doi:10.3390/jimaging9030070 · Cited by 9
“Brain-Tumor-MRI [75] is a data set of MR images containing different brain sections from 233 patients, created to develop CAD systems to diagnose the presence of brain tumours.”
BackgroundBrain Tumor Segmentation using Deep Learning Techniques: Comparative Analysis
Soewu, T., Soni, A., & Kaur, H. (2023) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp57993.2023.10134872 · Cited by 6
“The same dataset was used by Cheng et al in their paper titled “Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation” [13] and also “Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition” [14].”
MethodAzizy, F. M., Jondri, J., & Kurniawan, I. (2023) · doi:10.1109/icoict58202.2023.10262447 · Cited by 6
BRAIN TUMOR DETECTION AND CLASSIFICATION USING DEEP FEATURE FUSION AND STACKING CONCEPTS
Raza, S., Gul, N., Khattak, H. A., Rehan, A., Farid, M. I., Kamal, A., Rajput, D. J. S., Mukhtiar, S., & Ullah, A. (2024) · doi:10.53555/jptcp.v31i1.4179 · Cited by 7
Ghosh, A., Soni, B., & Baruah, U. (2024) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-024-00726-w · Cited by 8
Convolutional Neural Network-Based Automatic Brain Tumor Detection
Waghmare, V. K., & Kolekar, M. H. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7804-5_35 · Cited by 6
A Fuzzy-Entropy and Image Fusion Based Multiple Thresholding Method for the Brain Tumor Segmentation
Singh, P., Huang, Y., Chu, W., & Lee, J. H. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283171 · Cited by 6
“It is a region with a collection of prodigious brain cells and is classified as meningioma, glioma and pituitary tumors according to their growth [2].”
BackgroundMethodЗастосування текстурного аналізу у вирішенні задачі класифікації медичних зображень
Настенко, Є. А., Павлов, В., Nosovets, O. K., Круглий, В., Гончарук, М., Карлюк, А., Грішко, Д. Ю., Трофименко, О., & Babenko, V. O. (2020) · Біомедична інженерія і технологія · doi:10.20535/2617-8974.2020.4.221876 · Cited by 6
An Ensemble Classification Method for Brain Tumor Images Using Small Training Data
Nguyen, D. T., Nam, S. H., Batchuluun, G., Owais, M., & Park, K. R. (2022) · Mathematics · doi:10.3390/math10234566 · Cited by 6
“The second dataset is a public dataset that can be downloaded from its official website [29] and has been used in previous studies [8,30].”
MethodBrain MRI classification using discrete wavelet transform and bag-of-words
Ayadi, W., Elhamzi, W., Charfi, I., Ouni, B., & Atri, M. (2018) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/aset.2018.8379832 · Cited by 6
“[9] employed three techniques (GLCM, Intensity Histogram and BoW) to identify three cerebral tumors.”
MethodBrain Cancer Classification based on T1-Weighted Images Using Deep Learning
Hardjodipuro, R. D. R., Sugiyarto, M. A., Wigati, F. K., Gunawan, A. A. S., & Setiawan, K. E. (2024) · IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) · doi:10.1109/aims61812.2024.10513273 · Cited by 5
“Our study utilizes the same dataset to a research conducted by Cheng Jun et al. [11] They explored the Enhanced Performance of Brain Tumor Classification through Tumor Region Augmentation and Partition using data sourced from Nanfang Hospital in Guangzhou, China.”
MethodÜnlüleblebici, S., Kaçmaz, R. N., & Öztürk, C. (2026) · IEEE Access · doi:10.1109/access.2026.3659641 · Cited by 5
“Cheng et al. [32] explored brain tumor classification using three feature extraction strategies, namely density histograms, the gray-level co-occurrence matrix (GLCM), and the bag-of-words (BoW) model, to assess their comparative performance.”
A robust CNN classification of whole slide thyroid carcinoma images
El-Hossiny, A. S., Al‐Atabany, W., Hassan, O., Mostafa, A., & Sami, S. A. (2021) · International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) · doi:10.1109/jac-ecc54461.2021.9691433 · Cited by 5
Performance analysis of various deep learning techniques for brain tumor classification
Jaidka, P., & Jain, S. (2023) · International Conference on Power, Instrumentation, Energy and Control (PIECON) · doi:10.1109/piecon56912.2023.10085796 · Cited by 5
Improving Multi-class Brain Tumor Detection Using Vision Transformer as Feature Extractor
Abbasi, A. A., Hussain, L., & Ahmed, B. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-35078-8_1 · Cited by 5
AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection
Biradar, S., & Virupakshappa (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01199-3 · Cited by 5
“The dataset employed is gathered from the FigShare dataset [26, 27].”
FF-UNET: An Enhanced Framework for Improved Segmentation of Brain Tumors in MRI Images
Liu, J., Bhatti, U. A., Huang, M., Tang, H., Wu, Y., Feng, S., & Zhang, Y. (2024) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai62207.2024.10827496 · Cited by 5
Brain Tumor Detection Using YOLO Models in MRI Images
Wahidin, M. F., & Kosala, G. (2025) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis65852.2025.10933433 · Cited by 5
“The dataset utilized in this study comprises 3064 pairs of T1-weighted contrast-inhanced brain tumor images sourced from 233 patients with three kinds of brain tumor: meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices) [8], [9].”
InfluentialMRI Brain Tumor Identification and Classification Using Deep Learning Techniques
Chellakh, H., Moussaouı, A., Attıa, A., & Akhtar, Z. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280102 · Cited by 5
“The proposed methodology which presented in Figure 1”
MethodControl and Diagnosis of Brain Tumors Using Deep Neural Networks
Izadi, A., Hajati, F., Barzamini, R., Janpors, N., Farjad, B., & Barzamini, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28694-0_53 · Cited by 5
Brain Tumor Classification using a Convolutional Neural Network and Different Optimizers
Mulla, M., & Direkoğlu, C. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296659 · Cited by 5
“In [17], authors classify the brain tumors using an adaptive spatial division method to split augmented tumor regions into subregions which is followed by extracting some important features and their model achieved 91.28% highest accuracy.”
MethodInfluentialBrief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques
Mahmood, O. A., Yousif, A. S., & Adam, A. (2024) · Al-Kitab Journal for Pure Sciences · doi:10.32441/kjps.08.02.p8 · Cited by 5
Few-shot learning for inference in medical imaging with subspace feature representations
Liu, J., Fan, K., Cai, X., & Niranjan, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309368 · Cited by 9
“2) BrainTumor data [54, 55] is a four-category problem, consisting of 7, 022 images of human brain MRI images, three types of tumours (i.”
BackgroundMandle, A. K., Gupta, G. P., Sahu, S. P., Bansal, S., & Alhalabi, W. (2024) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.365910 · Cited by 5
Hybrid CNN-GCN Model for Tumor Classification: Integrating Spatial Relationships in Medical Imaging
Voddi, S., Sirisha, U., Praveen, S. P., Pandraju, T. K. S., Al-Dmour, N. A.-H., & Islam, S. (2024) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa63652.2024.10836627 · Cited by 5
Verma, A., & Yadav, A. K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20668-7 · Cited by 5
“Thefirst datasetusedinthisresearchconsistsof3064imagesofbraintumoursobtainedfrom233 patients[41].”
Gupta, M., Bhatia, S., Sharma, N., Verma, N., Sharma, S. K., & Singh, R. K. B. (2025) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/3147 · Cited by 5
Brain Tumor Image Classification by Randomly Wired Neural Networks with a Modified Method
Du, X., Chen, L., Liu, Z., Li, S., Liu, M., Yang, J. A., & Jin, L. (2020) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls49620.2020.9275242 · Cited by 5
“These MR images are taken from various angles and are provided by Guangzhou Southern Hospital and Tian-jin Medical University General Hospital in China [18] [19], which means that the dataset is really valid.”
MethodMeasure the superior functionality of machine intelligence in brain tumor disease prediction
Yadav, D. C., & Pal, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91197-9.00005-9 · Cited by 5
Interaction-Oriented Feature Decomposition for Medical Image Lesion Detection
Shen, J., Hu, Y., Zhang, X., Qiu, Z., Deng, T., Xu, Y., & Liu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_31 · Cited by 5
Brain Tumor Classification Using Deep Transfer Learning CNN Models
Singh, Y. P., & Lobiyal, D. K. (2022) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca54612.2022.9985654 · Cited by 5
“CNN is a Deep Learning (DL) technique often used to analyze visual input with low preprocessing requirements [16].”
MethodAhmed, A. S., & Salah, H. A. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v25.i1.pp273-280 · Cited by 5
Extreme Learning Bat Algorithm in Brain Tumor Classification
Sreekanth, G. R., Alrasheedi, A. F., Venkatachalam, K., Abouhawwash, M., & Askar, S. S. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.024538 · Cited by 5
Toufiq, D. M., Sagheer, A. M., & Veisi, H. (2022) · TEM Journal · doi:10.18421/tem112-17 · Cited by 5
Neural Memory Plasticity for Anomaly DetectionPreprint
Fernando, T., Denman, S., Ahmedt‐Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05448 · Cited by 5
“We use the MRI database provided by [46].”
MethodWITHDRAWN: A scrutiny on brain tumour classification using deep learning technique
Padmapriya, K., Divya, S., & Ezhumalai, P. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.11.561 · Cited by 5
RetractedBrain Tumor Classification from MRI using Vision Transformers EnsemblingPreprint
Tummala, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1593662/v1 · Cited by 5
Integrating pyramid vision transformer and topological data analysis for brain tumor
Joshi, D., Singh, B. K., Nagwanshi, K. K., & Choubey, N. S. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1463006 · Cited by 4
Balasubramani, K., & Shanmugavel, K. L. (2025) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-025-5671-y · Cited by 4
MDFE-Net: Multiscale dense feature extraction CNN for brain tumor classification from MRIs
Wu, H., & Al‐Huda, Z. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108978 · Cited by 4
Data Mining Techniques for Extraction and Analysis of Covid-19 Data
Al-Obadi, M. G., Farhan, H. M., Naseri, R. A. S., Türkben, A. K., Mustafa, A. K., & Al-Aloosi, A. R. (2022) · International Conference on Artificial Intelligence of Things (ICAIoT) · doi:10.1109/icaiot57170.2022.10121870 · Cited by 4
“In [24], they proposed a strategy to the characterization execution.”
BackgroundManjunath, P., Lerner, B., & Dunn, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66535-6_35 · Cited by 4
Brain Tumor MRI Images Classification Using Fine-Tuned Deep Learning Models
Tolba, M. A., Atia, B. S., William, M. A., Noureldin, A. K., Ragab, S. G., Mansy, S. N., Ramadan, M. A., & Mabrouk, M. S. (2024) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles63360.2024.10753158 · Cited by 4
“For instance, Cheng et al. [16] .”
Accurate Brain Tumor Detection Using Laplacian Based GAN Model
Raj, M., Kumar, A., Bansal, S., Singh, N., Jain, M., & Banoth, S. (2025) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt63985.2025.10986381 · Cited by 4
“[1] Due to the variety in tumor forms and sizes, established approaches such as Spatial Pyramid Matching (SPM) are less suitable for classifying brain tumors than they are for classifying natural scenes.”
Odong, O. J. P., Abo-Zahhad, M. M., & Abdelwahab, M. M. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06968-5 · Cited by 4
Alanazi, M. A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11561-3 · Cited by 4
“However, manual interpretation of MRI scans is a time-consuming and challenging task that requires expertise and is prone to human error and variability [3].”
InfluentialMRI Brain Tumor Classification Based on EfficientNet with Non-rigid Transformations
Gao, C., Wang, Z., Wang, W., & Zhang, J. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-5096-4_22 · Cited by 5
Self-citationNeelima, G., Satish, A. R., Maram, B., & Chigurukota, D. R. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2211890 · Cited by 4
An effective transfer learning model for multiclass brain tumor classification using MRI images
Jency Rubia, J., Lincy, B., Sheeba, P. T., & Shibi, C. S. (2023) · AIP conference proceedings · doi:10.1063/5.0170435 · Cited by 4
Medical Image Categorization Combining Image Segmentation and Machine Learning
Kareem, S. W., Almukhtar, F. H., Guron, A. T., & Salman, H. M. (2023) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec57380.2023.10438714 · Cited by 4
“MRI and computed tomography (CT) are the most often utilized non-invasive imaging modalities for capturing images of the human body [4] [5].”
BackgroundMethod
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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