Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

Abstract

Diabetic retinopathy (DR) is a common eye disease and a significant cause of blindness in diabetic patients. Regular screening with fundus photography and timely intervention is the most effective way to manage the disease. The large population of diabetic patients and their massive screening requirements have generated interest in a computer-aided and fully automatic diagnosis of DR. Deep neural networks, on the other hand, have brought many breakthroughs in various tasks in the recent years. To automate the diagnosis of DR and provide appropriate suggestions to DR patients, we have built a dataset of DR fundus images that have been labeled by the proper treatment method that is required. Using this dataset, we trained deep convolutional neural network models to grade the severities of DR fundus images. We were able to achieve an accuracy of 88.72% for a four-degree classification task in the experiments. We deployed our models on a cloud computing platform and provided pilot DR diagnostic services for several hospitals; in the clinical evaluation, the system achieved a consistency rate of 91.8% with ophthalmologists, demonstrating the effectiveness of our work. INDEX TERMS Diabetic retinopathy, automatic diagnosis, deep neural networks.

Evaluation of Conventional methods for the Detection of Lesions in Diabetic Retinopathy Images: A Research

“…They increased the dataset size i.e., 95% images of each of DIARETDB1, STARE, DRIVE databases and 94% images of MESSIDOR database and attained sensitivity/ specificity of 93.14%/ 93.19% for bright lesion detection and 94.01%/ 93.58% for dark lesion detection. Gao Z et al [57] addressed a deep convolutional neural network based DR classification system and achieved an average accuracy of 88.72% using the public datasets like DIARETDB0, DIARETDB1, DRIVE and STARE.…”

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

“…To assemble clinical application with 4 degree seriousness evaluation zhentao gao et.al [37] proposed CNN technique which is investigated on a moderate estimated new dataset and the model is hosted on a cloud platform, also used for pilot demonstrative administrations.…”

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

“…The authors reported that transfer learning increased model accuracy. Gao et al [70] used a private dataset with 4476 images with four classes. The authors cut the original images into four 300 * 300 partitions that were the input of four InceptionV3 networks, and then they concatenated the results to a single layer.…”

“…In studies [55,56,58,59,[61][62][63][68][69][70], the authors compared different architectures to determine the best performing one. In studies [56,59,61,63,70], the authors compared InceptionV3 architecture to other networks, and InceptionV3 achieved the best performance in all the studies except for [63]. The lowest performance was achieved by the AlexNet architecture in the following studies: [58,59,[61][62][63].…”