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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The Figshare dataset comprises 708 meningioma, 1426 glioma, and 930 pituitary images from the General Hospital and Nanfang Hospital, Tianjin Medical University, China.
“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]
The Figshare dataset contains 3064 T1-weighted contrast-enhanced images collected from 233 patients with a resolution of 512 × 512 pixels.
“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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Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE
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Deep convolutional neural networks with transfer learning for automated brain image classification
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Current AI technologies in cancer diagnostics and treatment
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A review of deep learning for brain tumor analysis in MRI
Dorfner, F. J., Patel, J., Kalpathy–Cramer, J., Gerstner, E. R., & Bridge, C. P. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00789-2 · Cited by 249 · see citing works
Edge U-Net: Brain Tumor Segmentation using MRI based on Deep U-Net Model with boundary information
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A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models
Aurna, N. F., Yousuf, M. A., Taher, K. A., Azad, A., & Moni, M. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105539 · Cited by 207 · see citing works
Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning
Almufareh, M. F., Imran, M., Khan, A., Humayun, M., & Asim, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3359418 · Cited by 200 · see citing works
“The Brain Tumor dataset of Southern Medical University, Guangzhou, is utilized for this research acquired from [24] and [53].”
MethodMulti-class classification of brain tumor types from MR images using EfficientNets
Zulfiqar, F., Bajwa, U. I., & Mehmood, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104777 · Cited by 200 · see citing works
Islam, T., Hafiz, M. S., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100340 · Cited by 216 · see citing works
A hybrid deep CNN model for brain tumor image multi-classification
Srinivasan, S., Francis, D., Mathivanan, S. K., Rajadurai, H., Shivahare, B. D., & Shah, M. A. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01195-7 · Cited by 188 · see citing works
“The Cancer Genome Atlas Low-Grade Glioma data-set is the third dataset that was analyzed (TCGA-LGG) [37], and it has 242,185 MRI images of patients with low-grade gliomas (G-I and G-II) and incorporates data from 198 patients.”
MethodBrain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network
Rizwan, M., Shabbir, A., Javed, A. R., Shabbir, M., Baker, T., & Al‐Jumeily, D. (2022) · IEEE Access · doi:10.1109/access.2022.3153108 · Cited by 187 · see citing works
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188 · see citing works
Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN
Khaliki, M. Z., & Başarslan, M. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-52823-9 · Cited by 182 · see citing works
“In this study, the accuracy result was 91.28% 14 .”
BackgroundInfluentialDifferential Deep Convolutional Neural Network Model for Brain Tumor Classification
Kader, I. A. E., Xu, G., Zhang, S., Saminu, S., Javaid, I., & Ahmad, I. S. (2021) · Brain Sciences · doi:10.3390/brainsci11030352 · Cited by 190 · see citing works
Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network
Bhanothu, Y., Kamalakannan, A., & Rajamanickam, G. (2020) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs48705.2020.9074375 · Cited by 177 · see citing works
“The MR image dataset for the proposed work was constructed from the dataset of 233 patients collected by Jun Cheng et al. [12, 13].”
MethodAn Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning
Ullah, N., Khan, J. A., Khan, M. S., Khan, W., Hassan, I., Obayya, M. I., Negm, N., & Salama, A. S. (2022) · Applied Sciences · doi:10.3390/app12115645 · Cited by 189 · see citing works
“[31] CNN–DWA 80% 98.00 Jun Cheng [4] BoW–SVM 80% 91.28 Ismael [24] DWT–Gabor NN 70% 91.90 Pashaei [16] CNN–ELM 70% 93.68 Abiwinanda [11] Proposed CNN 70% 84.19 Sawati [45] Block-wise fine tuning 25–50–75% 94.82 Arshia Rehman [17] Transfer Learning 70% 98.69 Proposed approach Inceptionresnetv2 80%…”
BackgroundResultInfluentialAsif, S., Zhao, M., Tang, F., & Zhu, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14828-w · Cited by 174 · see citing works
“Most of the latest models use the Figshare benchmark brain tumor dataset [11] to assess performance.”
MethodHuang, Z., Du, X., Chen, L., Li, Y., Liu, M., Chou, Y., & Jin, L. (2020) · IEEE Access · doi:10.1109/access.2020.2993618 · Cited by 159 · see citing works
“This dataset is collected from 233 patients and consists of 708 meningioma images, 1426 glioma images, and 930 pituitary tumor images [39], [40].”
MethodBrain Tumor Classification Using ResNet-101 Based Squeeze and Excitation Deep Neural Network
Ghosal, P., Nandanwar, L., Kanchan, S., Bhadra, A. K., Chakraborty, J., & Nandi, D. (2019) · International Conference on Advanced Computational and Communication Paradigms (ICACCP) · doi:10.1109/icaccp.2019.8882973 · Cited by 158 · see citing works
“The proposed method was tested and evaluated on the Brain tumor database which has been collected in the year from 2005 to 2010, from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China [12], [13].”
MethodInfluentialChelghoum, R., Ikhlef, A., Hameurlaine, A., & Jacquir, S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49161-1_17 · Cited by 156 · see citing works
“It has already used in previous works like in [32, 33].”
MethodÇELİK, M., & İni̇k, Ö. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122159 · Cited by 157 · see citing works
Rammurthy, D., & Mahesh, P. K. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.08.006 · Cited by 152 · see citing works
Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection
Qureshi, S. A., Raza, S. E. A., Hussain, L., Malibari, A., Nour, M. K., Rehman, A. U., Al‐Wesabi, F. N., & Hilal, A. M. (2022) · Applied Sciences · doi:10.3390/app12083715 · Cited by 157 · see citing works
“[9] used a publicly available T1-weighted Contrast-Enhanced Magnetic Resonance Images (CE-MRI) dataset [10], consisting of brain MRI scans having: glioma-; meningioma-; and pituitary-tumors, and applied three features extraction methods: intensity histogram; bag-of-words (BoW) model; and Gray Level Co-occurrence Matri…”
BackgroundMethodA New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI
Zahoor, M. M., Qureshi, S. A., Bibi, S., Khan, S. H., Khan, A., Ghafoor, U., & Bhutta, M. R. (2022) · Sensors · doi:10.3390/s22072726 · Cited by 150 · see citing works
Classification of brain tumors from MR images using deep transfer learning
Polat, Ö., & Güngen, C. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03572-9 · Cited by 146 · see citing works
Multi-level attention network: application to brain tumor classification
Shaik, N. S., & Cherukuri, T. K. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-02022-0 · Cited by 144 · see citing works
Rasheed, Z., Ma, Y.-K., Ullah, I., Ghadi, Y. Y., Khan, M. Z., Khan, M. A., Abdusalomov, A. B., Alqahtani, F. H., & Shehata, A. M. (2023) · Brain Sciences · doi:10.3390/brainsci13091320 · Cited by 140 · see citing works
“Brain tumors exhibit various sizes, dimensions, and densities [21].”
BackgroundHaq, A. U., Li, J., Khan, S., Alshara, M. A., Alotaibi, R. M., & Mawuli, C. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-19465-1 · Cited by 137 · see citing works
A hybrid image enhancement based brain MRI images classification technique
Ullah, Z., Farooq, M. U., Lee, S.-H., & An, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109922 · Cited by 134 · see citing works
A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization
Amou, M. A., Xia, K., Kamhi, S., & Mouhafid, M. (2022) · Healthcare · doi:10.3390/healthcare10030494 · Cited by 133 · see citing works
RanMerFormer: Randomized vision transformer with token merging for brain tumor classification
Wang, J., Lu, S., Wang, S., & Zhang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127216 · Cited by 131 · see citing works
An ensemble learning approach for brain cancer detection exploiting radiomic features
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105134 · Cited by 128 · see citing works
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144479 · Cited by 128 · see citing works
Self-citationBrain tumor classification based on hybrid approach
Ayadi, W., Charfi, I., Elhamzi, W., & Atri, M. (2020) · The Visual Computer · doi:10.1007/s00371-020-02005-1 · Cited by 127 · see citing works
“Other systems proposed in the literature [28] exploit a region segmentation for MRI image before using it to the proposed method.”
BackgroundAhmad, B., Sun, J., You, Q., Palade, V., & Mao, Z. (2022) · Biomedicines · doi:10.3390/biomedicines10020223 · Cited by 127 · see citing works
A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders
Lima, A. A., Mridha, M. F., Das, S. C., Kabir, M. M., Islam, M. R., & Watanobe, Y. (2022) · Biology · doi:10.3390/biology11030469 · Cited by 132 · see citing works
Kutlu, H., & Avcı, E. (2019) · Sensors · doi:10.3390/s19091992 · Cited by 120 · see citing works
“[31] focused on the classification of three types of brain tumors (i.”
BackgroundIslam, M., Reza, M. T., Kaosar, M. G., & Parvez, M. Z. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11014-1 · Cited by 120 · see citing works
“Data augmentation is a process that artificially generates more images while the primary input data remain the same [32].”
BackgroundReddy, C. K. K., Reddy, P. A., Janapati, H., Assiri, B., Shuaib, M., Alam, S., & Sheneamer, A. M. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1400341 · Cited by 121 · see citing works
Computer-assisted brain tumor type discrimination using magnetic resonance imaging features
Iqbal, S., Khan, M. U. G., Saba, T., & Rehman, A. U. (2017) · Biomedical Engineering Letters · doi:10.1007/s13534-017-0050-3 · Cited by 119 · see citing works
“[90] Custom developed 2005–2010 Nanfang Hospital and General Hospital, Tianjin Medical University”
BackgroundMethodFeature enhancement framework for brain tumor segmentation and classification
Tahir, B., Iqbal, S., Khan, M. U. G., Saba, T., Mehmood, Z., Anjum, A., & Mahmood, T. (2019) · Microscopy Research and Technique · doi:10.1002/jemt.23224 · Cited by 118 · see citing works
“These image-sets include Lung CT scans (Armato et al., 2015), Brain MRI (Cheng et al., 2015), and BRATS images (Menze et al., 2015).”
InfluentialJoint training of two-channel deep neural network for brain tumor classification
Bodapati, J. D., Shaik, N. S., Veeranjaneyulu, N., & Mundukur, N. B. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01793-2 · Cited by 115 · see citing works
“Inthefirst strategy, the suggested model is validated using the approach suggested (hold-out cross-validation) as in [22], where the dataset is partitionedinto70:15:15,respectively,fortrain,development and test sets and in the second strategy we adopt the fivefold cross-validation approach as in [12].”
MethodDutta, T. K., Nayak, D. R., & Zhang, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105421 · Cited by 123 · see citing works
Asiri, A. A. M., Shaf, A., Ali, T. M., Aamir, M., Irfan, M., Alqahtani, S., Mehdar, K. M., Halawani, H. T., Alghamdi, A., Alshamrani, A. F. A., et al. (2023) · Life · doi:10.3390/life13071449 · Cited by 112 · see citing works
“The research used the Figshare dataset in [22] to apply an adaptive spatial division algorithm to increase tumor areas as a zone of concern before further dividing into sub-sections and extracting the intensity histogram, grey-level co-occurrence matrix value, and a bag of words (BoW)…”
MethodHolistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network
Zhou, Y., Li, Z., Zhu, H., Chen, C., Gao, M., Xu, K. M., & Xu, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11723-8_21 · Cited by 108 · see citing works
Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion
Deepak, S., & Ameer, P. M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.039 · Cited by 108 · see citing works
An MRI-based deep learning approach for efficient classification of brain tumors
Haq, E. U., Jianjun, H., Li, K., Haq, H. U., & Zhang, T. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03535-9 · Cited by 107 · see citing works
“Studies conducted by Zacharaki et al. (2009) and Cheng et al. (2015) used feature engineering to effectively perform extraction of features, which were then used for a classification task after reducing their dimensions.”
MethodComparison of fine-tuning strategies for transfer learning in medical image classification
Davila, A., Colan, J., & Hasegawa, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105012 · Cited by 123 · see citing works
Brain tumor image generation using an aggregation of GAN models with style transfer
Mukherkjee, D., Saha, P., Kaplun, D., Sinitca, A., & Sarkar, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-12646-y · Cited by 106 · see citing works
“First, a side-by-side comparison of the images generated by these two models and AGGrGAN for the brain tumor dataset 34 is shown in Fig.”
BackgroundMethodInfluentialBayesCap: A Bayesian Approach to Brain Tumor Classification Using Capsule Networks
Afshar, P., Mohammadi, A., & Plataniotis, K. N. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3034858 · Cited by 105 · see citing works
“To test the proposed BayesCap, we used a benchmark brain cancer dataset [31] that includes 3,064 brain MRI images, diagnosed with one of the three tumor types, i,e.”
MethodBrain tumor classification utilizing deep features derived from high-quality regions in MRI images
Aamir, M., Rahman, Z., Abro, W. A., Bhatti, U. A., Dayo, Z. A., & Ishfaq, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104988 · Cited by 105 · see citing works
Bačanin, N., Zivkovic, M. Z., Al-Turjman, F. M., Venkatachalam, K., Trojovský, P., Strumberger, I., & Bezdan, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-09744-2 · Cited by 104 · see citing works
“The benchmark dataset (https:// figsh are. com/ artic les/ datas et/ brain_ tumor_ datas et/ 15124 27) is comprised of 3064 T1-weighted MRIs with Glioma, Meningioma, and Pituitary brain tumor classes, obtained from 233 subjects by Cheng et. al 65 .”
MethodNaeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
“The first dataset was acquired from brain tumor patients referred to the General Hospital from 2005 to 2010, and the second dataset was collected from Nanfang Hospital, Tianjin Medical University, China.”
BackgroundMüezzinoğlu, T., Bayğın, N., Tuncer, I., Barua, P. D., Bayğın, M., Doğan, Ş., Tuncer, T., Palmer, E. E., Cheong, K. H., & Acharya, U. R. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00789-x · Cited by 105 · see citing works
“The dataset presented by Cheng et al. [27] contains 3064 images belonging to three classes: GBM (1426), Meningioma (708), and Pituitary (930) [53].”
BackgroundAlqhtani, S. M., Soomro, T. A., Ali, A., Memon, A. A., Irfan, M., Rahman, S., Jalalah, M., Almawgani, A. H. M., & Eljak, L. A. B. (2024) · IEEE Access · doi:10.1109/access.2024.3394541 · Cited by 101 · see citing works
“Utilizing software-based tools alongside MRI scans facilitates the segmentation, identification, and categorization of tumors, expediting treatment strategies and contributing to improved patient survival rates [18], [21], [22], [23].”
MethodCapsNet topology to classify tumours from brain images and comparative evaluation
Göçeri, E. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0312 · Cited by 100 · see citing works
A framework for efficient brain tumor classification using MRI images
Guan, Y., Aamir, M., Rahman, Z.-U., Ali, A., Abro, W. A., Dayo, Z. A., Bhutta, M. S., & Hu, Z. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021292 · Cited by 107 · see citing works
Basha, J., Bačanin, N., Vukobrat, N., Zivkovic, M. Z., Venkatachalam, K., Hubálovský, Š., & Trojovský, P. (2021) · Sensors · doi:10.3390/s21196654 · Cited by 100 · see citing works
“Finally, Figure 5 shows three brain tumor cases from the second dataset utilized in this research [81].”
BackgroundMethodCombining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
Aloraini, M., Khan, A. A., Aladhadh, S., Habib, S., Alsharekh, M. F., & Islam, M. (2023) · Applied Sciences · doi:10.3390/app13063680 · Cited by 110 · see citing works
- 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
- Life Sciences › Neuroscience › Neurology › 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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