Latest Research in Brain Tumor Detection and Classification

Top Research Topics in Brain Tumor Detection and Classification

• Brain Tumor Detection and Classification — 6 papers
• Computer Vision and Pattern Recognition — 3 papers
• Advanced Neural Network Applications — 2 papers
• Machine Learning in Bioinformatics — 1 papers
• Image and Video Processing — 1 papers
• Medical Image Segmentation Techniques — 1 papers
• Neural Networks Stability and Synchronization — 1 papers

Highest-Cited Papers

1. FPGA-accelerated brain tumor segmentation: a fixed-point modified fuzzy C-means approach with skipped membership RAM
2. NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI
3. An Efficient Brain Tumor Segmentation and Classification Framework Using Recurrent Unet + + and Adaptive Inception Net with Exponential Tanh Function
4. A dynamic quantum clustering approach to brain tumor segmentation (2 citations)
5. Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026
6. An explainable hybrid CNN–vision transformer framework for brain tumor detection and classification using MRI
7. BRAIN TUMOR SEGMENTATION OF MRI SEQUENCES (T1, T2, T1CE, FLAIR) USING BRATS DATASET
8. Hybrid encoder-decoder to preserve long-range context in brain tumor segmentation with EfficientNetB7 and a convolutional vision transformer mechanism
9. BRAIN TUMOR SEGMENTATION USING U-NET-BASED DEEP LEARNING MODELS
10. NeuroAttnFuseNet dual branch attention fusion for brain tumor MRI classification
11. When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning
12. Enhancing MRI Brain Tumor Edge Detection: A Hybrid Preprocessing Approach Utilizing CLAHE
13. Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation
14. Active vision for brain tumor localization on grid-discretized MRI using a dual-map actor-critic network with proximal policy optimization
15. A novel detection-guided approach for multimodal brain tumor segmentation