Tongue Segmentation for Disease Diagnosis a Precise and Fast method using Double U-Net Architecture

Abstract

Abstract: A robust automatic tongue diagnosis system greatly relies on accurate segmentation of the tongue body from the image. It is a challenge to accurately segment tongue body from an image having close interference of teeth, lips and face. Deep Learning methods such as Deep Convolution Networks like FCN (fully connected Networks), U-NET, Res-Net (Residual Network) have superseded the performance of conventional techniques like Active Contour, Gradient V Flow, Level Set etc. . In this paper looking into the tremendous capability of Deep neural Networks, we have employed Double U-NET for tongue image segmentation of images captured by mobile device and compared the results with that of U-NET, and Res U-NET architectures. Qualitative as well as quantitative analysis of the three reveals a superior performance of Double U-net especially in images with additional dominant features of the face such as lips, teeth, and spaces with the tongue image. Keywords: Double U-Net, Residual U-Net, U-Net, tongue segmentation, automatic tongue diagnosis