The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Open accessAbstract
Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.
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“Data collected from three different kinds of brain tumors consisted of 708 meningiomas, 1426 gliomas, and 930 pituitaries images from the General Hospital and Nanfang Hospital, Tianjin Medical University, China [72].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
In the citing paper
“This contains 3064 T1-weighted contrast-enhanced images collected from 233 patients with a resolution of 512 × 512 pixels.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
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Cites
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“(cid:129) Local features: The next step is to extract local features within the ROI, such as scale-invariant feature transform (SIFT) descriptor [20] and raw patch [21].”
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“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.”
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“As pointed out in [29,30], it is beneficial to capture a certain amount of context.”
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“The BoW method was originally used in the text retrieval domain, and it has been successfully adapted to the visual analysis domain [13,14].”
MethodLocality-constrained Linear Coding for image classification
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Pande, T. (2026) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss68683.2026.11526530
“In particular, the application of deep learning to brain imaging has resulted in the implementation of such technologies to tumor segmentation, classification [6], and survival prediction tasks.”
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Towards Development of an Optimized Brain Tumor Classification Model
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Brain Tumor Classification Using Computer Vision and Deep Learning Techniques
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The Explanatory Deep Learning Model for Brain Tumor Detection Based on MRI Images
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“MLS encompasses a wide variety of lesion types, such as wet age-related macular degeneration [23], brain tumors [12], adenocarcinoma [33], thyroid nodules [55], colorectal polyps [35], lung infection [17], breast lesions [1], and skin lesions [14].”
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Cai, C., Zhang, J., Zhang, D., Chen, W., Tan, X., Du, G., & Jiang, H. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1986322
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Smart Brain Tumor Classification Using Deep Learning with Channel Attention Networks
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“Because incorrect classifications can significantly affect the choice of treatment and prognosis, reliable multiclass identification of brain tumors is crucial [1].”
Ali, M., Hashmi, U., Azam, M., & Sarwar, R. (2026) · Vector The Journal of Engineering · doi:10.1049/tje2.70241
An Explainable XAIFusion Deep Learning Framework for Brain Tumour MRI Classification
Vedanarayanan, V., Sakthi Prabha, R., Rajinikanth, E., Gomathi, T., S.Manodhiya, & Aranganathan, A. (2026) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11724036
“ResNet18 is fine-tuned on the publicly available Cheng et al. dataset [6].”
Duong, L. T., Maswita, P. W., Sumaryada, T. I., Yani, S., Tài, D. T., Omer, H. B., Tamam, N. M., & Sulieman, A. (2026) · Diagnostics · doi:10.3390/diagnostics16213485
Saifullah, S., Dreżewski, R., & Yudhana, A. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03348-2
“Three benchmark datasets were employed: (i) FBTS, consisting of T1-CE MRI slices with meningioma, glioma, and pituitary tumor classes [70 ]; (ii) BraTS 2021, containing multimodal scans comprising T1, T1CE, T2, and FLAIR sequences with tumor-subregion annotations subsequently converted into binary…”
Gayathridevi, R., & Venkatesan, R. (2026) · National Academy Science Letters · doi:10.1007/s40009-026-02464-5
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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