1 research outputs found
Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks
The change of retinal vasculature is an early sign of many vascular and
systematic diseases, such as diabetes and hypertension. Different behaviors of
retinal arterioles and venules form an important metric to measure the disease
severity. Therefore, an accurate classification of arterioles and venules is of
great necessity. In this work, we propose a novel architecture of deep
convolutional neural network for segmenting and classifying arterioles and
venules on retinal fundus images. This network takes the original color fundus
image as inputs and multi-class labels as outputs. We adopt the
encoding-decoding structure (Unet) as the backbone network of our proposed
model. To improve the classification accuracy, we develop a special encoding
path that couples InceptionV4 modules and Cascading Dilated Convolutions (CDCs)
on top of the backbone network. The model is thus able to extract and fuse
high-level semantic features from multi-scale receptive fields. The proposed
method has outperformed the previous state-of-the-art method on DRIVE dataset
with an accuracy of 0.955 0.002