IMFF-Net: An integrated multi-scale feature fusion network for accurate retinal vessel segmentation from fundus images

Mingtao Liu; Yunyu Wang; Lei Wang; Shunbo Hu; Xing Wang; Qingman Ge

doi:10.1016/j.bspc.2024.105980

ScienceGate Book Chapters

JOURNAL ARTICLE

IMFF-Net: An integrated multi-scale feature fusion network for accurate retinal vessel segmentation from fundus images

Mingtao Liu Yunyu Wang Lei Wang Shunbo Hu Xing Wang Qingman Ge

Year: 2024 Journal: Biomedical Signal Processing and Control Vol: 91 Pages: 105980-105980 Publisher: Elsevier BV

DOI: 10.1016/j.bspc.2024.105980

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Fundus (uterus) Segmentation Scale (ratio) Feature (linguistics) Retinal Fusion Computer vision Pattern recognition (psychology) Ophthalmology Cartography Geography

Metrics

Cited By

22.11

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Retinal Imaging and Analysis

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Digital Imaging for Blood Diseases

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Retinal and Optic Conditions

Health Sciences → Medicine → Ophthalmology

IMFF-Net: An integrated multi-scale feature fusion network for accurate retinal vessel segmentation from fundus images

Metrics

Citation History

Topics

Related Documents

U-Net Fundus Retinal Vessel Segmentation Method Based on Multi-scale Feature Fusion

SAF-NET: Split Attention Fusion Network for retinal vessel segmentation from fundus images

MDAC-Net: A Multi-Scale Dense Attention Cascade Network for Retinal Vessel Segmentation From Fundus Images

MIC-Net: multi-scale integrated context network for automatic retinal vessel segmentation in fundus image

A multi-scale feature extraction and fusion-based model for retinal vessel segmentation in fundus images