ArticlePloS one2015
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 143 papers, 3 of them syntheses that pooled it.
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Who cites it
143 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.International journal of computer assisted radiology and surgery · 2021Pooled it
- MRI-based brain tumor detection through an explainable EfficientNetV2 and MLP-mixer attention architecture.Physical and engineering sciences in medicine · 2026Article
- A class-wise quantum relational calibration network for brain tumor diagnosis.Scientific reports · 2026Article
- Explainable deep learning techniques for microscopic fungi classification using a learnable threshold-based ReLULeaky activation function and transfer learning.Journal of pathology informatics · 2026Article
- Cloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging.Cureus · 2026Article
- BG-YOLO11s: Boundary-Guided YOLO11 with Bézier Contour Augmentation for Brain Tumour Segmentation in T1-CE MRI.Diagnostics (Basel, Switzerland) · 2026Article
- AUCp: Pseudo-AUC for Inference Model Selection With Unlabeled Validation Data in Abnormality Detection.IEEE transactions on medical imaging · 2026Article
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.Brain sciences · 2026Review
- Brain tumor classification from MRI images using a multi-scale channel attention CNN integrated with SVM.Scientific reports · 2026Article
- Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging.Frontiers in radiology · 2026Review
- An efficient brain tumor classification system with accurate recognition and complementary downsampling blocks.PloS one · 2026Article
- Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI.Frontiers in artificial intelligence · 2026Article
- Evaluating the detection of small brain lesions in magnetic resonance using deep learning.Frontiers in neuroscience · 2026Article
- Few-Shot and Zero-Shot Learning for MRI Brain Tumor Classification Using CLIP and Vision Transformers.Sensors (Basel, Switzerland) · 2025Article
- PSF-GRBM: brain tumor classification and grading using optimized gated recurrent unit-deep bidirectional long short-term memory.Journal of neuro-oncology · 2025Article
- Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN.Biomedicines · 2025Article
83 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.