ArticleJournal of imaging2021
A Survey of Brain Tumor Segmentation and Classification Algorithms.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation.Visual computing for industry, biomedicine, and art · 2026Article
- Article
- Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification.PloS one · 2026Article
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- A novel deep neural architecture for efficient and scalable multidomain image classification.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Computer vision based efficient segmentation and classification of multi brain tumor using computed tomography images.Scientific reports · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- A Web-Deployed, Explainable AI System for Comprehensive Brain Tumor Diagnosis.Neurology international · 2025Article
- A novel hybrid vision UNet architecture for brain tumor segmentation and classification.Scientific reports · 2025Article
- Review
- Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.Radiology. Artificial intelligence · 2024Article
- Dual view deep learning for enhanced breast cancer screening using mammography.Scientific reports · 2024Article
- Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.PloS one · 2024Article
- G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design.PloS one · 2024Article
- Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.JMIR medical informatics · 2023Article
- Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics.
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Registered trials
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