Exudate Detection in Diabetic Retinopathy Using Deep Learning Techniques

In this paper three different types of architectures have been trained and tested to determine which one of them is more efficient in detecting exudates from retinal images. The database that was used is SUSTech_SYSU from which 100 images were extracted for image testing and another 100 images that were used in obtaining the training dataset. The three architectures that were studied in this paper are SqueezeNet, ResNet50 and GoogLeNet. The results showed that from the three architectures the most efficient in exudates detection was ResNet50 with an average accuracy of 0.928, followed by SqueezeNet with 0.910 and finally, GoogLeNet for which the average accuracy was 0.907.