ArticleBiomedicines2024
Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model.Scientific reports · 2026Article
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- High-precision brain tumor segmentation with switchable normalization in faster R-CNN architecture.Scientific reports · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
- Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors.Biomedicines · 2026Article
- Mod-SE(2): a geometric deep learning framework for brain tumor classification and segmentation in MRI images.Journal of biomedical science · 2026Article
- Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification.PloS one · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- A denoising autoencoder-enhanced ResNet 50 framework for explainable brain tumor classification using MRI.Frontiers in oncology · 2026Article
- Efficient attention-based Ghost-ResNet for brain tumor classification in magnetic resonance imaging (MRI).Frontiers in neuroscience · 2026Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images.Cancers · 2025Article
- An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features.Scientific reports · 2025Article
- Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.Scientific reports · 2025Article
- CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability.Diagnostics (Basel, Switzerland) · 2025Article
- Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer's Disease MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Application of algorithms based on improved YOLO in MRI image detection of brain tumors.Frontiers in neurology · 2025Article
- Multi-scale based Network and Adaptive EfficientnetB7 with ASPP: Analysis of Novel Brain Tumor Segmentation and Classification.Current medical imaging · 2025Article
- CerevianNet: parameter efficient multi-class brain tumor classification using custom lightweight CNN.Frontiers in medicine · 2025Article
- A novel decision ensemble framework: Attention-customized BiLSTM and XGBoost for speculative stock price forecasting.PloS one · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.
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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.