Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model
Machine learning has significantly improved disease diagnosis, enhancing the efficiency and accuracy of the healthcare system. One critical area where it proves beneficial is diagnosing brain tumors, a life-threatening disease, where early and accurate predictions can save lives. This study focuses...
Saved in:
| Main Authors: | Bhargav Mallampati, Abid Ishaq, Furqan Rustam, Venu Kuthala, Sultan Alfarhood, Imran Ashraf |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10332149/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans
by: Yu Lyu, et al.
Published: (2025-01-01) -
Visualizing UNet Decisions: An Explainable AI Perspective for Brain MRI Segmentation
by: D. Jeya Mala, et al.
Published: (2025-01-01) -
Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms
by: Rakesh Salakapuri, et al.
Published: (2025-07-01) -
Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification
by: Syed Sajid Hussain, et al.
Published: (2025-01-01) -
Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI
by: Annachiara Cariola, et al.
Published: (2025-07-01)