Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment
Objectively assessing the perceptual quality of an ocular fundus image is essential for the reliable diagnosis of various ocular diseases. A fair amount of work has been done in this field to date. However, the generalizability of the current work is limited, as the existing quality models were deve...
Main Authors: | Aditya Raj, Nisarg A. Shah, Anil Kumar Tiwari, Maria G. Martini |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9044361/ |
Similar Items
-
Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs
by: Jaemin Son, et al.
Published: (2021-01-01) -
Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms
by: Parham Khojasteh, et al.
Published: (2018-11-01) -
A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation
by: Jianwei Lu, et al.
Published: (2018-11-01) -
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD
by: Muhammad Mateen, et al.
Published: (2018-12-01) -
Multi-label classification of fundus images based on graph convolutional network
by: Yinlin Cheng, et al.
Published: (2021-07-01)