DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach.
DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach.
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DOI：
10.1109/access.2020.3025010
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Guizani M
中科院分区：
文献类型：
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作者：
Sakib S;Tazrin T;Fouda MM;Fadlullah ZM;Guizani M
随着新冠肺炎(2019年冠状病毒病)大流行呈指数级增长，临床医生除了病毒和抗体检测方式外，继续寻求准确和快速的诊断方法。由于X射线和计算机体层摄影(CT)扫描等射线检查具有成本效益，而且在公共卫生设施、医院急诊室(ER)甚至农村诊所都可以广泛使用，因此它们可以用于快速检测可能由新冠肺炎引发的肺部感染。因此，为了实现新冠肺炎检测的自动化，本文提出了一种可行、高效的基于深度学习的胸片分类框架，以较高的准确率将新冠肺炎病例与其他异常(如肺炎)和正常病例区分开来。一个独特的数据集是从四个可公开获得的来源准备的，其中包含新冠肺炎、肺炎和正常病例的X射线后前位(PA)胸片。我们提出的DL-CRC框架通过自适应地使用产生式对抗网络(GAN)和通用数据增强方法来生成合成的新冠肺炎感染的胸部X光图像来训练健壮的模型，从而利用针对新冠肺炎数据的射线图像数据增强(DAI)算法。将由实际胸部X光图像和合成胸部X光图像组成的训练数据输入到我们定制的卷积神经网络DL-CRC模型中，该模型实现了93.94%的新冠肺炎检测准确率，而在没有数据扩充的情况下(即当原始数据集中只有几个实际的新冠肺炎图像样本时)，新冠肺炎检测的准确率为54.55%。此外，我们通过将定制的CNN模型与文献中广泛采用的CNN体系结构进行广泛的比较来证明我们的定制CNN模型的合理性，这些体系结构是代表基于深度、基于多路径和混合的CNN范式的ResNet、初始-ResNet v2和DenseNet。我们的建议具有令人鼓舞的高分类准确率，这意味着它可以高效地从X光图像中自动检测新冠肺炎，从而提供肺部新冠肺炎感染的快速可靠证据，从而补充现有的新冠肺炎诊断模式。
With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.