ArticleFrontiers in human neuroscience2023
Pre-trained deep learning models for brain MRI image classification.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- Recent Advancements of Transcranial Direct Current Stimulation and Machine Learning: Methods, Challenges, and Opportunities.Transactions on artificial intelligence · 2026Article
- Single Image Haze Removal via Multiple Variational Constraints for Vision Sensor Enhancement.Sensors (Basel, Switzerland) · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.Neuroradiology · 2025Review
- Use of artificial intelligence in neurological disorders diagnosis: A scientometric study.World journal of methodology · 2025Article
- Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.Scientific reports · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Leptomeningeal metastatic disease: new frontiers and future directions.Nature reviews. Clinical oncology · 2025Review
- The Frontiers of Smart Healthcare Systems.Healthcare (Basel, Switzerland) · 2024Review
- Classification of Multi-Parametric Body MRI Series Using Deep Learning.IEEE journal of biomedical and health informatics · 2024Article
- Deep learning methods for diagnosis of graves' ophthalmopathy using magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model.Bioengineering (Basel, Switzerland) · 2024Article
- Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.Journal of cancer research and clinical oncology · 2024Article
- Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives.Sensors (Basel, Switzerland) · 2024Review
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm.Frontiers in oncology · 2024Article
- A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.Frontiers in physiology · 2024Article
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2 authors.
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Abstract
Brain tumors are serious conditions caused by uncontrolled and abnormal cell division. Tumors can have devastating implications if not accurately and promptly detected. Magnetic resonance imaging (MRI) is one of the methods frequently used to detect brain tumors owing to its excellent resolution. In the past few decades, substantial research has been conducted in the field of classifying brain images, ranging from traditional methods to deep-learning techniques such as convolutional neural networks (CNN). To accomplish classification, machine-learning methods require manually created features. In contrast, CNN achieves classification by extracting visual features from unprocessed images. The size of the training dataset had a significant impact on the features that CNN extracts. The CNN tends to overfit when its size is small. Deep CNNs (DCNN) with transfer learning have therefore been developed. The aim of this work was to investigate the brain MR image categorization potential of pre-trained DCNN VGG-19, VGG-16, ResNet50, and Inception V3 models using data augmentation and transfer learning techniques. Validation of the test set utilizing accuracy, recall, Precision, and F1 score showed that the pre-trained VGG-19 model with transfer learning exhibited the best performance. In addition, these methods offer an end-to-end classification of raw images without the need for manual attribute extraction.
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