A Novel Dynamic Residual Self-Attention Transfer Adaptive Learning Fusion Approach for Brain Tumor Diagnosis
Source abstract
Abstract
A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. Fo... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning and ELM · Brain Tumor Detection and Classification · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
1. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-based brain tumor classification10.1007/s10586-024-04532-1 ↗
2. Refining neural network algorithms for accurate brain tumor classification in MRI imagery10.1186/s12880-024-01285-6 ↗
3. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 5010.1186/s12880-024-01292-7 ↗
4. Brain tumor detection and classification using deep feature fusion and stacking concepts
5. Brain tumor detection and classification using IFF-FLICM segmentation and optimized ELM model10.1155/2024/8419540 ↗
6. Brain tumor detection and classification using transfer learning models
7. Deep learning for enhanced brain tumor detection and classification10.1016/j.rineng.2024.102117 ↗
8. Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN10.1038/s41598-024-52823-9 ↗
9. MRI brain tumor detection using deep learning and machine learning approaches
10. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks10.1038/s41598-024-59566-7 ↗
11. SSGNet: selective multi-scale receptive field and kernel self-attention based on group-wise modality for brain tumor segmentation10.3390/electronics13101915 ↗
12. A deep analysis of brain tumor detection from MR images using deep learning networks10.3390/a16040176 ↗
13. An effective diagnosis system for brain tumor detection and classification10.32604/csse.2023.036107 ↗
14. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques10.1186/s12911-023-02114-6 ↗
15. Accurate brain tumor detection using deep convolutional neural network
16. Ensemble of deep learning models for brain tumor detection
17. Brain tumor segmentation based on a new threshold approach
18. Brain tumor detection using machine learning
19. Exploring the power of deep learning: fine-tuned vision transformer for accurate and efficient brain tumor detection in MRI scans10.3390/diagnostics13122094 ↗
20. Deep convolutional neural networks model-based brain tumor detection in brain MRI images
21. Deep learning-based framework for tumour detection and semantic segmentation
22. Brain tumor automatic detection from MRI images using transfer learning model with deep convolutional neural network10.21608/jaet.2020.42896.1051 ↗
23. Accurate detection of brain tumor using optimized feature selection based on deep learning techniques10.1007/s11042-023-15239-7 ↗
24. Identification of brain tumor on MR images with and without segmentation using Dl techniques
25. Brain tumor detection using integrated learning process detection (ILPD)
26. Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction
27. Vision S. 2030; 2016. [cited 2024 May 29]. Available from: https://www.vision2030.gov.sa/.
28. Understanding self-attention mechanism via dynamical system perspective
29. Schettler D. 2021. [cited 2025 Jan 30]. Available from: https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1.
30. Awsaf. 2020 [cited 2025 Jan 30]. Available from: https://www.kaggle.com/datasets/awsaf49/brats2020-training-data.
31. Cheng J. Brain tumor dataset. Figshare: Dataset; 2017. [cited 2025 Jan 30]. Available from: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427/5.
32. Chakrabarty N. 2019. [cited 2025 Jan 30]. Available from: https://kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection.
33. Hamada A. 2022. [cited 2025 Jan 30]. Available from: https://kaggle.com/datasets/ahmedhamada0/brain-tumor-detection.
34. Bhuvaji S. 2020. [cited 2025 Jan 30]. Available from: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri.
35. Introducing a deep learning method for brain tumor classification using MRI Data towards better performance10.1016/j.imu.2023.101423 ↗
36. Employing deep learn-ing and transfer learning for accurate brain tumor detection10.1038/s41598-024-57970-7 ↗