School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Int J Med Inform. 2020 Dec;144:104284. doi: 10.1016/j.ijmedinf.2020.104284. Epub 2020 Sep 23.
This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.
CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.
The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).
This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.
本研究旨在开发和测试一种新的计算机辅助诊断（CAD）方案，以检测胸部 X 光图像中的冠状病毒（COVID-19）感染性肺炎。
CAD 方案首先应用两个图像预处理步骤去除大部分横膈膜区域，使用直方图均衡化算法和双边低通滤波器处理原始图像。然后，将原始图像和两个滤波图像组合成伪彩色图像。将该图像输入基于迁移学习的卷积神经网络（CNN）模型的三个输入通道，将胸部 X 光图像分为 COVID-19 感染性肺炎、其他社区获得性非 COVID-19 感染性肺炎和正常（非肺炎）三种类型。为了构建和测试 CNN 模型，使用了一个公开的数据集，该数据集包含 8474 张胸部 X 光图像，分别有 415、5179 和 2880 例属于三个类别。数据集按照每个类别的病例频率随机分为训练、验证和测试三个子集，用于训练和测试 CNN 模型。
基于 CNN 的 CAD 方案在对三个类别的分类中，整体准确率为 94.5%（2404/2544），95%置信区间为[0.93，0.96]。CAD 方案在分类有无 COVID-19 感染的病例时，灵敏度为 98.4%（124/126），特异性为 98.0%（2371/2418）。但是，如果不使用两个预处理步骤，CAD 的分类准确率则会降低至 88.0%（2239/2544）。
本研究表明，添加两个图像预处理步骤并生成伪彩色图像在开发胸部 X 光图像深度学习 CAD 方案以提高 COVID-19 感染性肺炎检测准确性方面发挥了重要作用。
