MPEDA-Net: A lightweight brain tumor segmentation network using multi-perspective extraction and dense attention-Reference-Cited by-同舟云学术

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MPEDA-Net: A lightweight brain tumor segmentation network using multi-perspective extraction and dense attention

Published:2024-05 Issue: Volume:91 Page:106054
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Luo Hao^ORCID,Zhou Dongmei,Cheng Yongjian,Wang Siqi

Publisher

Elsevier BV

Reference61 articles.

1. Benign vs malignant tumors;Patel;JAMA Oncol.,2020

2. Reflections on the current status of commercial automated segmentation systems in clinical practice;Sykes;J. Med. Radiat. Sci.,2014

3. MR brain segmentation based on DE-ResUnet combining texture features and background knowledge;Wu;Biomed. Signal Process. Control.,2022

4. Medical image analysis using convolutional neural networks: a review;Anwar;J. Med. Syst.,2018

5. Scale-adaptive super-feature based MetricUNet for brain tumor segmentation;Liu;Biomed. Signal Process. Control.,2022

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1. MSCF-Net: Attention-Guided Multi-Scale Context Feature Network for Ship Segmentation in Surveillance Videos;Mathematics;2024-08-20

2. GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency;Physics in Medicine & Biology;2024-05-23

3. TransSea: Hybrid CNN–Transformer With Semantic Awareness for 3-D Brain Tumor Segmentation;IEEE Transactions on Instrumentation and Measurement;2024

4. Study on Brain Tumor Image Classification Based on Improved MobileViT Model;Computer Science and Application;2024

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