Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy. (10th September 2018)

• Record Type: Journal Article
• Authors: Lin, Gen-Min; Chen, Mei-Juan; Yeh, Chia-Hung; Lin, Yu-Yang; Kuo, Heng-Yu; Lin, Min-Hui; Chen, Ming-Chin; Lin, Shinfeng D.; Gao, Ying; Ran, Anran; Cheung, Carol Y.
• Other Names: Cerviño Alejandro Academic Editor.
• Is Part Of: Journal of ophthalmology. Volume 2018(2018)
• Publication Date: 2018-09-10
• Subjects: Ophthalmology -- Periodicals; Eye Diseases; Ophthalmology; Ophthalmology; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 617.7
• DOI: 10.1155/2018/2159702
• Languages: English
• ISSNs: 2090-004X

Abstract

Abstract : Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21, 123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33, 000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33, 000 photographs, 30, 000 images were randomly selected as the training set, and the remaining 3, 000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (allp values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.