U-Net-ASPP: U-Net based on atrous spatial pyramid pooling model for medical image segmentation in COVID-19

Xiaojing Qiu

doi:10.6180/jase.202212_25(6).0012

U-Net-ASPP: U-Net based on atrous spatial pyramid pooling model for medical image segmentation in COVID-19

Computer Science and Information Engineering

Xiaojing Qiu This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹School of Nursing, Henan Technical Institute, Zhengzhou 450000, China

Received: December 15, 2021
Accepted: January 1, 2022
Publication Date: February 10, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202212_25(6).0012

ABSTRACT

In this paper, we propose a novel U-Net model based on atrous spatial pyramid pooling for medical image segmentation. In the encoder, a pre-trained ImageNet Efficientnet-B0 network is used to extract features for effective information. Atrous spatial pyramid pooling (ASPP) is used to extract the above multi-scale information between encoding and decoding. Finally, the captured information is cascaded with the information of the encoding layer, and the segmentation accuracy is improved by combining the attention mechanism. Experimental results on public data sets show that the accuracy, recall rate and Dice coefficient of the proposed algorithm are 85.35

Keywords: U-Net; atrous spatial pyramid pooling; Efficientnet-B0 network; attention mechanism; COVID-19 medical image segmentation

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