Abstract

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3,486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

中文翻译：

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通过使用 CNN 特征的堆叠模型进行 COVID-19 感染诊断的分类

摘要

COVID-19 大流行影响了全世界数百万人，从一开始就导致数十万人死亡。检查还发现，即使 COVID-19 患者最初在冠状病毒中幸存下来，病毒留下的肺炎仍可能导致严重的疾病，导致器官衰竭，从而在未来死亡。本研究的目的是通过机器学习方法使用胸部 X 射线图像对 COVID-19、正常和病毒性肺炎进行分类。来自三个类别的总共 3,486 张胸部 X 射线图像首先通过支持向量机 (SVM)、逻辑回归 (LR)、人工神经网络 (ANN) 模型等三个单机器学习模型进行分类，然后通过堆叠模型进行分类它是通过组合这 3 个单一模型而创建的。计算了召回率、精度、F-score 和准确率等几个性能评估指标，以评估和比较 3 个单四模型和研究中使用的最终堆叠模型的分类性能。作为评估的结果，SVM、ANN、LR 和堆叠模型分别达到了 90.2%、96.2%、96.7% 和 96.9% 的分类准确率。研究结果表明，所提出的堆叠模型是一种快速且廉价的辅助 COVID-19 诊断方法，可以帮助医生和护士在繁忙的临床环境中更好、更有效地诊断 COVID-19 感染病例。作为评估的结果，SVM、ANN、LR 和堆叠模型分别达到了 90.2%、96.2%、96.7% 和 96.9% 的分类准确率。研究结果表明，所提出的堆叠模型是一种快速且廉价的辅助 COVID-19 诊断方法，可以帮助医生和护士在繁忙的临床环境中更好、更有效地诊断 COVID-19 感染病例。作为评估的结果，SVM、ANN、LR 和堆叠模型分别达到了 90.2%、96.2%、96.7% 和 96.9% 的分类准确率。研究结果表明，所提出的堆叠模型是一种快速且廉价的辅助 COVID-19 诊断方法，可以帮助医生和护士在繁忙的临床环境中更好、更有效地诊断 COVID-19 感染病例。