Application of convolution neural networks in eye fundus image analysis

In this work, we proposed a new approach to analyzing eye fundus images that relies upon the use of a convolutional neural network (CNN). The CNN architecture was constructed, followed by network learning on a balanced dataset composed of four classes of images, composed of thick and thin blood vessels, healthy areas, and exudate areas. The learning was conducted on 12x12 images because an experimental study showed them to be optimal for the purpose. The test error was no higher than 4% for all sizes of the samples. Segmentation of eye fundus images was performed using the CNN. Considering that exudates are a primary target of laser coagulation surgery, the segmentation error was calculated on the exudate class, amounting to 5%. In the course of this research, the HSL color system was found to be most informative, using which the segmentation error was reduced to 3%.