Brain tumor segmentation by deep learning transfer methods using MRI images
Brain tumor segmentation is one of the most challenging tasks of medical image analysis. The diagnosis of patients with gliomas is based on the analysis of magnetic resonance images and manual segmentation of tumor boundaries. However, due to its time-consuming nature, there is a need for a fast and...
Saved in:
Main Author: | E.Y. Shchetinin |
---|---|
Format: | Article |
Language: | English |
Published: |
Samara National Research University
2024-06-01
|
Series: | Компьютерная оптика |
Subjects: | |
Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480315e.html |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An attention based residual U-Net with swin transformer for brain MRI segmentation
by: Tazkia Mim Angona, et al.
Published: (2025-03-01) -
Ulnar variance detection from radiographic images using deep learning
by: Sahar Nooh, et al.
Published: (2025-02-01) -
A deep ensemble learning framework for glioma segmentation and grading prediction
by: Liang Wen, et al.
Published: (2025-02-01) -
Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model
by: Changan Yang, et al.
Published: (2025-02-01) -
IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT
by: Jayapradha J, et al.
Published: (2025-06-01)