Chowdhury Nihad K, Rahman Md Muhtadir, Kabir Muhammad Ashad
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW Australia.
Health Inf Sci Syst. 2020 Sep 21;8(1):27. doi: 10.1007/s13755-020-00119-3. eCollection 2020 Dec.
The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient's chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest X-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more significant features expeditiously related to suspected disease. The experimental results demonstrate that our proposed method significantly improves performance metrics: the accuracy, precision, recall and F1 scores reach , , and , respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems.
新冠疫情持续严重破坏全球卫生系统的繁荣。为抗击这一疫情，针对感染患者的有效筛查技术必不可少。毫无疑问，利用胸部X光图像进行放射学评估是重要的筛查技术之一。一些早期研究表明，新冠病毒感染患者的胸部X光图像显示出异常情况，这对于感染新冠病毒的患者来说是正常现象。在本文中，我们提出了一种基于并行扩张卷积神经网络（CNN）的新冠病毒检测系统，用于从胸部X光图像中检测新冠病毒，名为并行扩张新冠网络（PDCOVIDNet）。首先，对公开可用的胸部X光图像集进行充分预加载和增强，然后用所提出的方法进行分类。以并行形式采用不同的卷积扩张率证明了使用PDCOVIDNet提取新冠病毒检测放射学特征的原理。因此，我们用两种可视化方法辅助我们的方法，这两种方法专门设计用于增进对与新冠病毒感染相关关键成分的理解。这两种可视化方法都针对与最后一个卷积层的特征图相关的给定图像类别计算梯度，以创建一个类别判别区域。在我们的实验中，我们总共使用了2905张胸部X光图像，包括三种病例（如新冠病毒感染、正常和病毒性肺炎），实证评估表明所提出的方法能迅速提取与疑似疾病更显著相关的特征。实验结果表明，我们提出的方法显著提高了性能指标：准确率、精确率、召回率和F1分数分别达到 、 、 和 ，与现有最先进方法相比具有可比性或有所提高。我们相信我们的贡献能够支持抗击新冠疫情，并将在基于人工智能的系统中用于新冠病毒筛查。
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