Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network
Joint Authors
Lin, Gen-Min
Pao, Shu-I
Lin, Hong-Zin
Chien, Ke-Hung
Chen, Jiann-Torng
Tai, Ming-Cheng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-20
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR).
The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system.
In this paper, the entropy image computed by using the green component of fundus photograph is proposed.
In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images.
The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.
American Psychological Association (APA)
Pao, Shu-I& Lin, Hong-Zin& Chien, Ke-Hung& Tai, Ming-Cheng& Chen, Jiann-Torng& Lin, Gen-Min. 2020. Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network. Journal of Ophthalmology،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1189861
Modern Language Association (MLA)
Pao, Shu-I…[et al.]. Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network. Journal of Ophthalmology No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1189861
American Medical Association (AMA)
Pao, Shu-I& Lin, Hong-Zin& Chien, Ke-Hung& Tai, Ming-Cheng& Chen, Jiann-Torng& Lin, Gen-Min. Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network. Journal of Ophthalmology. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1189861
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189861