ArticleJournal of digital imaging2022
Combined Features in Region of Interest for Brain Tumor Segmentation.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Article
- Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- A novel approach for the detection of brain tumor and its classification via independent component analysis.Scientific reports · 2025Article
- Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.Scientific reports · 2025Article
- Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans.Frontiers in dental medicine · 2025Article
- A Novel Fragmentation-based Approach for Accurate Segmentation of Small-Sized Brain Tumors in MRI Images.Current medical imaging · 2025Article
- The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.Journal of imaging · 2024Review
- Machine learning and deep learning for brain tumor MRI image segmentation.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.Scientific reports · 2023Article
- Integrating deep learning and thermal estimation for enhanced MRI-based brain tumor diagnosis.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.
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