REC-ResNet： Feature enhancement model for COVID-19 aided diagnosis

ZHOU Tao; LIU Yuncan; HOU Senbao; YE Xinyu; LU Huiling

doi:10.37188/OPE.20233114.2093

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REC-ResNet： Feature enhancement model for COVID-19 aided diagnosis

Information Sciences | 更新时间：2023-08-26

- REC-ResNet： Feature enhancement model for COVID-19 aided diagnosis
- Optics and Precision Engineering Vol. 31, Issue 14, Pages: 2093-2110(2023)
- 作者机构：
  
  1.北方民族大学计算机科学与工程学院，宁夏银川 750021
  2.北方民族大学图像图形智能处理国家民委重点实验室，宁夏银川 750021
  3.宁夏医科大学理学院，宁夏银川 750004
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233114.2093
  CLC：
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ZHOU Tao, LIU Yuncan, HOU Senbao, et al. REC-ResNet： Feature enhancement model for COVID-19 aided diagnosis. [J]. Optics and Precision Engineering 31(14):2093-2110(2023)
DOI：

ZHOU Tao, LIU Yuncan, HOU Senbao, et al. REC-ResNet： Feature enhancement model for COVID-19 aided diagnosis. [J]. Optics and Precision Engineering 31(14):2093-2110(2023) DOI： 10.37188/OPE.20233114.2093.

摘要

基于残差神经网络的新冠肺炎（Corona Virus Disease 2019， COVID-19）辅助诊断是最近的研究热点，但是COVID-19胸部X-Ray图像病变区域多样化，其大小、形状、位置因患者而异，且与周围组织的边界模糊，对比度较低，导致难以充分提取病变区域的有效特征。本文针对上述问题，提出一种COVID-19辅助诊断模型REC-ResNet，该模型以ResNet50为主干网络，引入三种特征增强策略，用来提高模型的特征提取能力。首先，采用残差自适应特征融合模块以自适应加权的方式有效地融合模型每个Stage中来自不同残差块的分层特征，该模块不仅建模不同通道之间的相关性，还学习自适应地估计不同层次信息的相对重要性；然后，在模型主干中引入高效特征增强Transformer模块，采用特征增强多头自注意力提取胸部X-Ray图像中的全局信息以增强模型的表达能力，有效地弥补了CNN捕获全局特征表示能力较弱的不足；其次，为了获得更丰富的上下文信息，提出跨层注意力增强模块，分别采用通道注意力和空间注意力对深层和浅层特征进行增强，并在充分考虑长距离特征依赖的情况下有效地融合高级语义信息和低级空间细节实现跨层注意力特征增强，使模型提取更多有效特征以进一步提高模型分类准确率。最终，在COVID-19胸部X-Ray图像数据集上的实验结果表明：本文模型与其他先进的CNN分类模型相比能够实现优异的分类性能，Acc，Pre，Rec，F1 Score和Spe指标分别为97.58%，97.60%，97.58%，97.59%和97.46%，进一步通过Grad-CAM可视化技术对模型进行解释，以增强特征的直观性。本文方法有助于临床医生做出正确的医学判断和更好的患者预后，为COVID-19的辅助诊断提供有效的帮助。

Abstract

关键词

新冠肺炎胸部X-Ray图像残差神经网络注意力机制特征增强

Keywords

COVID-19chest X-Ray imageresidual neural networkattention mechanismfeature enhancement

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