ArticleSensors (Basel, Switzerland)2021
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 2 of them syntheses that pooled it.
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Who cites it
100 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.European urology oncology · 2026Article
- Explainable hybrid AI CAD framework for advanced prediction of steel surface defects.Scientific reports · 2026Article
- Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.Interdisciplinary sciences, computational life sciences · 2026Article
- LHW-Net: An ensemble-based machine learning framework for brain tumor classification.PloS one · 2026Article
- Enhanced Brain Tumor Classification with Convolutional Neural Networks.Advances in experimental medicine and biology · 2026Article
- SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering.Frontiers in neuroscience · 2026Article
- An efficient brain tumor classification system with accurate recognition and complementary downsampling blocks.PloS one · 2026Article
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application.BJR artificial intelligence · 2026Article
- XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing.Scientific reports · 2025Article
- PSF-GRBM: brain tumor classification and grading using optimized gated recurrent unit-deep bidirectional long short-term memory.Journal of neuro-oncology · 2025Article
- A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features.Scientific reports · 2025Article
- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.Scientific reports · 2025Article
- Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.Scientific reports · 2025Article
- Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.Scientific reports · 2025Article
- A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning.Scientific reports · 2025Article
- Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.Scientific reports · 2025Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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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.