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COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation
International Journal of Imaging Systems and Technology ( IF 3.3 ) Pub Date : 2021-06-04 , DOI: 10.1002/ima.22611
XiaoQing Zhang 1 , GuangYu Wang 2 , Shu-Guang Zhao 2
Affiliation  

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.

中文翻译:

COVSeg-NET:用于 COVID-19 肺部 CT 图像分割的深度卷积神经网络

COVID-19是一种新型呼吸道传染病,对全世界人类的生存构成严重威胁。使用人工智能技术分析 COVID-19 患者的肺部图像,可以实现快速有效的检测。本研究提出了一种 COVSeg-NET 模型,可以准确分割 COVID-19 肺部 CT 图像中的磨玻璃不透明病变。COVSeg-NET模型基于全卷积神经网络模型结构,主要包括卷积层、非线性单元激活函数、最大池化层、批量归一化层、合并层、扁平化层、sigmoid层等。通过实验和评估结果可以看出,COVSeg-NET模型的dice系数、敏感性和特异性分别为0.561、0.447和0.996,比其他深度学习方法更先进。COVSeg-NET 模型可以使用更小的训练集和更短的测试时间来获得更好的分割结果。
更新日期:2021-08-05
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