Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

TLDR

The study aims to determine whether neural networks can detect diabetic retinopathy features in fundus images and compare their performance to an ophthalmologist. A back‑propagation neural network was trained on 147 diabetic and 32 normal fundus images, with digital filtering and network parameter variations evaluated, and then tested on 200 diabetic and 101 normal images to assess detection performance. The network achieved 88.4 % sensitivity and 83.5 % specificity for diabetic retinopathy, with vessel, exudate, and haemorrhage detection rates of 91.7 %, 93.1 %, and 73.8 %, indicating good accuracy and potential as a screening aid.

Abstract

To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images.147 diabetic and 32 normal images were captured from a fundus camera, stored on computer, and analysed using a back propagation neural network. The network was trained to recognise features in the retinal image. The effects of digital filtering techniques and different network variables were assessed. 200 diabetic and 101 normal images were then randomised and used to evaluate the network's performance for the detection of diabetic retinopathy against an ophthalmologist.Detection rates for the recognition of vessels, exudates, and haemorrhages were 91.7%, 93.1%, and 73.8% respectively. When compared with the results of the ophthalmologist, the network achieved a sensitivity of 88.4% and a specificity of 83.5% for the detection of diabetic retinopathy.Detection of vessels, exudates, and haemorrhages was possible, with success rates dependent upon preprocessing and the number of images used in training. When compared with the ophthalmologist, the network achieved good accuracy for the detection of diabetic retinopathy. The system could be used as an aid to the screening of diabetic patients for retinopathy.