A Tongue Segmentation Algorithm Based on Deeplabv3+ Network Model

Abstract

When collecting tongue images in an open en- vironment with a mobile portable collection device, there will be problems of different shooting angles and unstable lighting. Due to the strong mobility of the portable acquisition device, the captured images will inevitably be blurred by jitter, which further increases the difficulty of segmentation. This paper applies neural network to tongue images segmentation, and proposes a tongue images segmentation method based on deep convolutional neural network. This method is a tongue images segmentation method based on the semantic segmentation framework of DeeplabV3+. First, we modify the output category of the network. Because only the tongue region is segmented, segmentation targets can be divided into two categories when performing tongue images segmentation. One is the tongue region and the other is the background region. Then we replace the backbone network of DeeplabV3+ with a lightweight network and add an attention mechanism. Finally, we use the collected tongue images in the open environment to train the network. After the network obtains the initial segmentation result, tongue images are restored according to the same type of label, so as to obtain the required tongue images only containing tongues. The experimental results show that the method has higher segmentation accuracy for tongue images in open environment, and can better meet the needs of people for tongue images segmentation.