论文标题
从公共Covid-19数据集提取的深度功能的决策和特征水平融合
Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets
论文作者
论文摘要
冠状病毒（Covid-19）是一种传染性的肺部疾病，已经影响了数百万人，并被世卫组织宣布为全球大流行。由于Covid-19的高度传染性及其在患者中导致严重疾病的可能性很大，因此快速，准确的诊断工具的发展变得非常重要。实时逆转录聚合链反应（RT-PCR）用于使用粘液和唾液混合物样品来检测冠状病毒RNA的存在。但是，RT-PCR遭受低敏感性，尤其是在早期阶段。因此，由于其快速成像速度，显着低成本和辐射剂量低的剂量暴露，因此在Covid-19的早期诊断中，胸部射线照相的使用一直在增加。在我们的研究中，已经提出了基于卷积神经网络（CNN）的X射线图像的计算机辅助诊断系统，该系统可以被放射科医生用作COVID-19检测的支持工具。通过使用CNN提取的深度特征集是为特征水平融合而成的，并在决策水平融合思想方面将其馈送到多个分类器，以区分Covid-19，肺炎和无发现类别。在决策水平融合的想法中，将多数投票计划应用于分类器的结果决策。为三个逐步创建的数据集提供了获得的精度值和基于混淆矩阵的评估标准。已经讨论了所提出的方法优于现有的CoVID-19检测研究的各个方面，并通过使用类激活映射技术在视觉上验证了所提出的方法的融合性能。实验结果表明，所提出的方法已经达到了高共证实的检测性能，通过其可比的准确性和优质/回忆值通过现有研究证明了这一点。
The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs), which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using CNNs were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.
