Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.

中文翻译：

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ADID-UNET——肺部 CT 扫描的 COVID-19 感染分割模型

目前，新型冠状病毒病（COVID-19）是威胁世界的最大健康危机之一。计算机断层扫描（CT）扫描的自动检测是检测肺部感染的经典方法，但它面临着强度变化大、肺部感染区域附近边缘不清晰以及数据采集过程中存在噪声等问题。因此，本文提出了一种新的 COVID-19 肺部感染分割深度网络，称为 Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET)。稠密网络代替卷积和最大池化函数来增强特征传播并解决梯度消失问题。改进的扩张卷积用于增加编码器输出的感受野，以进一步从小的感染区域获得更多的边缘特征。将注意力门集成到模型中可以抑制背景并提高预测精度。实验结果表明，ADID-UNET 模型可以准确分割 COVID-19 肺部感染区域，准确性、特异性和骰子系数 (DC) 等指标的性能指标均超过 80%。此外，与其他最先进的架构相比，所提出的模型显示出出色的分割效果，DC 和 F1 得分分别为 0.8031 和 0.82。