ReviewBiomedical engineering letters2018
Computer-assisted brain tumor type discrimination using magnetic resonance imaging features.
Review in Biomedical engineering letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
- A novel approach for the detection of brain tumor and its classification via independent component analysis.Scientific reports · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.Journal of imaging · 2024Review
- AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings.Plant methods · 2024Article
- Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Mesenchymal stem cell-derived exosomes as a new therapeutic strategy in the brain tumors.Stem cell research & therapy · 2022Review
- AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.Life (Basel, Switzerland) · 2022Article
- Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance.Applied bionics and biomechanics · 2022Article
- Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.Scientific reports · 2021Article
- Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons.IT professional · 2021Article
- Are radiomics features universally applicable to different organs?Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks.Medical & biological engineering & computing · 2021Article
- CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.Frontiers in neuroinformatics · 2021Article
- Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.Brain sciences · 2020Review
- Multivariate association between brain function and eating disorders using sparse canonical correlation analysis.PloS one · 2020Article
- Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.Journal of medical systems · 2019Article
- Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network.Biomedical engineering letters · 2018Article
- Machine learning in biomedical engineering.Biomedical engineering letters · 2018Article
- MRI image analysis methods and applications: an algorithmic perspective using brain tumors as an exemplar.Neuro-oncology advancesReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
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Registered trials
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.