Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation
Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high perfo...
Main Authors: | Md. Mohaimenul Islam, Tahmina Nasrin Poly, Bruno Andreas Walther, Hsuan Chia Yang, Yu-Chuan (Jack) Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-04-01
|
Series: | Journal of Clinical Medicine |
Subjects: | |
Online Access: | https://www.mdpi.com/2077-0383/9/4/1018 |
Similar Items
-
MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation
by: Yun Jiang, et al.
Published: (2022-07-01) -
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks
by: Yun Jiang, et al.
Published: (2019-09-01) -
Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation
by: Yan Lv, et al.
Published: (2020-01-01) -
Retinal Vessel Segmentation Using Deep Learning: A Review
by: Chunhui Chen, et al.
Published: (2021-01-01) -
Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network
by: Yun Jiang, et al.
Published: (2019-01-01)