NCBI PubMed Central
open access
A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI#1077712
- Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations#924753
- Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations#920519
- An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.#327108
- On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation#799192
Record · ID 362193
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive