Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy

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 (all p 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.

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

“…We select 21,123 color fundus photographs with good image quality. e experimental setup is the same with that in [18]. e retinal images obtained from the Kaggle e resized images are increased by data augmentation using flipping and rotation.…”

“…Artificial intelligence has the potential to revolutionize the traditional diagnosis method for eye disease and bring out a significant clinical impact on promoting ophthalmic health care service [10][11][12][13]. Automated DR detections have been previously studied [14][15][16][17][18]. Deep learning of fundus photograph has emerged as a practical technique for automatic screening and diagnosis of DR. e effective deep learning system is able to correctly and automatically identify severer DR with equal or better accuracy than the trained graders and retina specialists [19], and thus, it can benefit the patients in medically underserved areas that have limited numbers of ophthalmologists and rare medical resources.…”

“…e convolutional neural network (CNN), a core model of deep learning in computer vision, has yielded impressive results in terms of prediction and diagnosis in medical image classification. e entropy image of luminance of fundus photograph, which involves measuring the complexity of a retinal image, has been demonstrated to increase the detection performance of referable DR for a CNN-based system in [18].…”

Recent Innovations in Automated Detection and Classification of Diabetic Retinopathy

“…Their project achieved accuracy of 99.3% for training and 88.3% for the testing set. Lin et al [15] in their work used deep learning to compare the performance of detection for severe diabetic retinopathy between entropy images and original fundus images. This study showed that using entropy images achieve 86.10%of accuracy, 73.24% of sensitivity, and 93.81% of specificity, whereas achieve 81.80% of accuracy, 68.36%of sensitivity, and 89.87%of specificity while using original fundus images.…”

Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy

“…The first issue is expertise: a well-trained ophthalmologist is required for DR grading and lesion type assessment. 8,9 The second issue is intergrader reliability: human interpretation of imaging varies among ophthalmologists. 10,11 The third issue is manpower: the compound annual growth rate (CAGR) of the number of eye doctors (CAGR: 2.60%) is lower than that of the diabetic population in Taiwan (CAGR: 4.78%).…”