Objective

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.

Method

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.

Results

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).

Conclusion

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.

中文翻译：

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通过预处理算法提高 CNN 使用胸部 X 射线图像预测 COVID-19 可能性的性能

 客观的

本研究旨在开发和测试一种新的胸部 X 射线图像计算机辅助诊断 (CAD) 方案，以检测冠状病毒 (COVID-19) 感染的肺炎。

 方法

CAD方案首先应用两个图像预处理步骤来去除大部分隔膜区域，使用直方图均衡算法和双边低通滤波器处理原始图像。然后，使用原始图像和两个滤波图像来形成伪彩色图像。该图像被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 射线图像分为 3 类：COVID-19 感染肺炎、其他社区获得性非 COVID-19 感染肺炎、和正常（非肺炎）病例。为了构建和测试 CNN 模型，使用了包含 8474 张胸部 X 射线图像的公开数据集，其中分别包括三类 415、5179 和 2,880 个病例。数据集被随机分为 3 个子集，即针对每个类别中相同频率的案例进行训练、验证和测试，以训练和测试 CNN 模型。

 结果

基于 CNN 的 CAD 方案在对 3 个类别进行分类时，总体准确率为 94.5% (2404/2544)，置信区间为 [0.93,0.96] 为 95%。在对感染和未感染 COVID-19 的病例进行分类时，CAD 的敏感性 (124/126) 为 98.4%，特异性为 98.0% (2371/2418)。然而，如果不使用两个预处理步骤，CAD 的分类精度较低，为 88.0% (2239/2544)。

 结论

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 射线图像的深度学习 CAD 方案以提高检测 COVID-19 感染肺炎的准确性方面发挥着重要作用。