Neural networks for exudate detection in retinal images

Diabetic retinopathy is a common eye disease directly associated with diabetes and one of the leading causes for blindness. One of its early indicators is the presence of exudates on the retina. In this paper we present a neural network-based approach to automatically detect exudates in retina images. A sliding windowing technique is used to extract parts of the image which are then passed to the neural net to classify whether the area is part of an exudate region or not. Principal component analysis and histogram specification are used to reduce training times and complexity of the network, and to improve the classification rate. Experimental results on an image data set with known exudate locations show good performance with a sensitivity of 94.78% and a specificity of 94.29%.