Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images

Abstract

Medical diagnosis is being assisted by numerous expert systems that have been developed to increase the accuracy of such diagnoses. The development of image processing techniques along with the rapid development in areas like machine learning and computer vision help in creating such expert systems that almost nearly match the accuracy of the expert human eye. The medical condition of diabetic retinopathy is diagnosed by analyzing the retinal blood vessels for damages, abnormal new growths and ruptures. Various techniques using convolutional neural networks have been used to segment retinal blood vessels from fundus images, but these techniques often do not segment the retinal blood vessels accurately and add additional noise due to the limited receptive field of the convolutional filters. The limited receptive field of the convolutional layer prevents the convolutional neural network from getting an accurate context of objects that extend beyond the size of the filter. The proposed architecture uses a dilated convolutional filter to obtain a larger receptive field which leads to a greater accuracy in segmenting the retinal blood vessels with near human accuracy. The convolutional neural networks were trained using the popular datasets. The proposed architecture produced an area under ROC curve (AUC) of 0.9794 and an accuracy of 95.61% and required very few iterations to train the network.