Abdel-Basset Mohamed, Hawash Hossam, Moustafa Nour, Elkomy Osama M
Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt.
School of Engineering and Information Technology, University of New South Wales @ ADFA, Canberra, ACT 2600, Australia.
Pattern Recognit Lett. 2021 Dec;152:311-319. doi: 10.1016/j.patrec.2021.10.027. Epub 2021 Oct 29.
COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.
新冠病毒病（COVID-19）仍在威胁全球卫生基础设施。计算机断层扫描（CT）已被证明是识别、量化和诊断此类疾病的一种信息丰富的工具。迫切需要设计高效的深度学习（DL）方法，以便在肺部CT扫描中自动定位并区分COVID-19与其他类似的肺炎。因此，本研究引入了一种新颖的两阶段DL框架，根据CT切片中检测到的感染区域来区分COVID-19与社区获得性肺炎（CAP）。首先，提出了一种新颖的U型网络来分割出现感染的肺部区域。然后，将迁移学习的概念应用于特征提取网络，以增强网络学习疾病模式的能力。之后，通过注意力机制捕获并汇总多尺度信息，以实现强大的分类性能。第三，我们提出了一个感染预测模块，该模块利用感染位置来指导分类决策，从而提供可解释的分类决策。最后，在公共数据集上对所提出的模型进行了评估，该模型取得了出色的分割和分类性能，优于前沿研究。
