Segmentation of lung parenchyma based on new U-NET network Online publication date: Mon, 24-Oct-2022
by Liying Cheng; Longtao Jiang; Xiaowei Wang; Zuchen Liu; Shuai Zhao
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 23, No. 2, 2022
Abstract: As the risk of lung disease increases in people's daily lives and COVID-19 spreads around the world, lung screening has become critical. Owing to the unique lung tissue, traditional image segmentation methods are difficult to achieve accurate segmentation of lung tissues. In view of the complexity of lung tissue structure, it was found in the experiment that the segmentation accuracy of upper lung and lower lung parenchyma tissue was low. Aiming at this phenomenon, a new network model, new U-NET, was proposed based on the improvement and optimisation of U-NET network model. Experimental data show that the proposed new U-NET network model solves the problem of low segmentation accuracy of the original U-NET network segmentation model at both ends of lung, improves the segmentation accuracy of lung parenchyma on the whole, and verifies that the new U-NET network model is more suitable for parenchyma segmentation.
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