YOLO-TumorNet: An innovative model for enhancing brain tumor detection performance
Brain tumors are high-risk conditions where early detection and precise localization are crucial for improving patient prognosis. However, existing automated detection methods still exhibit limitations in robustness within complex backgrounds, boundary recognition, and the detection of small tumors,...
Saved in:
Main Authors: | Jian Huang, Wen Ding, Tiancheng Zhong, Gang Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-04-01
|
Series: | Alexandria Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000894 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SAlexNet: Superimposed AlexNet using residual attention mechanism for accurate and efficient automatic primary brain tumor detection and classification
by: Qurat-ul-ain Chaudhary, et al.
Published: (2025-03-01) -
PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection
by: Pratikkumar Chauhan, et al.
Published: (2025-01-01) -
A 3D Dual Encoder Mirror Difference ResU-Net for Multimodal Brain Tumor Segmentation
by: Qiwei Xing, et al.
Published: (2025-01-01) -
Application of MRI image segmentation algorithm for brain tumors based on improved YOLO
by: Tao Yang, et al.
Published: (2025-01-01) -
Decoding brain tumor insights: Evaluating CAM variants with 3D U-Net for segmentation
by: Dian Nova Kusuma Hardani, et al.
Published: (2024-12-01)