scholarly journals Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-Angle Retinal Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 10232-10241 ◽  
Author(s):  
Yinsheng Zhang ◽  
Li Wang ◽  
Zhenquan Wu ◽  
Jian Zeng ◽  
Yi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Retinopathy Of Prematurity ◽  
Deep Neural Network ◽  
Retinal Images ◽  
Wide Angle ◽  
Screening System ◽  
Automated Screening

Detection of exudates using neuro-fuzzy technique

2020 ◽  
pp. 002072091989755
Author(s):  
J Jeyapriya ◽  
KS Umadevi
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Large Population ◽  
Retinal Images ◽  
Hybrid Neural Network ◽  
Noisy Images ◽  
Neuro Fuzzy ◽  
Image Set ◽  
Automated Screening ◽  
Clinical Potential

Diabetic retinopathy is a common condition of an ailment where the retina is harmed on the grounds that fluid breaks away from the walls of blood vessels into the retina. The large population is affected by diabetic retinopathy and hence research in the area of automated screening has acquired major research interest in the scientific community. The diagnosing features for diabetic retinopathy comprises of identifying deformations of minute blood vessel in form of exudates and its features occurring in and around the regions of fundus which will result into exudes, hemorrhages, or microaneurysms. This feature is often hard to be classified by naked eyes and need trained personnel for close inspection before making diagnosis. Therefore, a comprehensive machine learning tool is in the demand for automated diabetic retinopathy detection. Neural networks have given promising results in the field of classification. Therefore, we present a hybrid neural network-based method with realistic clinical potential. In this study, we present a neuro-fuzzy technique, which is developed for identification of exudates in retinal images and is utilized for the extraction of essential features from the retinal images of patient’s eyeballs. We trained this hybrid neural network base technique over a set of 25,600 training images for color retinal images and tested it over 12,000 testing image set for binocular model. The method achieves an accuracy of 95.4% recognition on non-noisy images and 93% on noisy images; thereby giving a consistency rate of 91% in correspondence with the ophthalmologist.

Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network

2019 ◽  
pp. 379-396 ◽  
Author(s):  
Ricky Parmar ◽  
Ramanathan Lakshmanan ◽  
Swarnalatha Purushotham ◽  
Rajkumar Soundrapandiyan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Neural Network ◽  
Retinal Images

scholarly journals Automatic Diagnosis of Familial Exudative Vitreoretinopathy Using a Fusion Neural Network for Wide-Angle Retinal Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 162-173
Author(s):  
Yu Ye ◽  
Jianbo Mao ◽  
Lei Liu ◽  
Shulin Zhang ◽  
Lijun Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Retinal Images ◽  
Wide Angle ◽  
Automatic Diagnosis ◽  
Familial Exudative Vitreoretinopathy
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