Fang Zhenyu, Ren Jinchang, MacLellan Calum, Li Huihui, Zhao Huimin, Hussain Amir, Fortino Giancarlo
School of Computer SciencesGuangdong Polytechnic Normal University Guangzhou 510065 China.
School of Computer Software and MicroelectronicsNorthwestern Polytechnical University Xi'an 710072 China.
IEEE Trans Mol Biol Multiscale Commun. 2021 Jul 26;8(1):17-27. doi: 10.1109/TMBMC.2021.3099367. eCollection 2022 Mar.
To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due to the limited data, existing models suffer from incapable feature extraction and poor network convergence and optimization. Accordingly, a multi-stage residual network, MSRCovXNet, is proposed for effective detection of COVID-19 from chest x-ray (CXR) images. As a shallow yet effective classifier with the ResNet-18 as the feature extractor, MSRCovXNet is optimized by fusing two proposed feature enhancement modules (FEM), i.e., low-level and high-level feature maps (LLFMs and HLFMs), which contain respectively more local information and rich semantic information, respectively. For effective fusion of these two features, a single-stage FEM (MSFEM) and a multi-stage FEM (MSFEM) are proposed to enhance the semantic feature representation of the LLFMs and the local feature representation of the HLFMs, respectively. Without ensembling other deep learning models, our MSRCovXNet has a precision of 98.9% and a recall of 94% in detection of COVID-19, which outperforms several state-of-the-art models. When evaluated on the COVIDGR dataset, an average accuracy of 82.2% is achieved, leading other methods by at least 1.2%.
为抑制新型冠状病毒肺炎（COVID-19）的传播，早期准确诊断至关重要，除实时逆转录聚合酶链反应（RT-PCR）拭子检测外，胸部X光成像筛查也发挥着重要作用。由于数据有限，现有模型存在特征提取能力不足、网络收敛和优化不佳的问题。因此，提出了一种多阶段残差网络MSRCovXNet，用于从胸部X光（CXR）图像中有效检测COVID-19。作为一个以ResNet-18为特征提取器的浅层但有效的分类器，MSRCovXNet通过融合两个提出的特征增强模块（FEM）进行优化，即低级和高级特征图（LLFM和HLFM），它们分别包含更多的局部信息和丰富的语义信息。为了有效融合这两个特征，分别提出了单阶段FEM（MSFEM）和多阶段FEM（MSFEM）来增强LLFM的语义特征表示和HLFM的局部特征表示。在不集成其他深度学习模型的情况下，我们的MSRCovXNet在检测COVID-19时的精度为98.9%，召回率为94%，优于几个先进模型。在COVIDGR数据集上进行评估时，平均准确率达到82.2%，领先其他方法至少1.2%。
