通过机器学习使用胸片检测 Covid-19

Umar S. Alqasemi, Abdullah Al Baiti
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引用次数: 0

摘要

最近由严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)引起的大流行凸显了早期检测感染的重要性,尤其是在 RT-PCR 检测设备匮乏的情况下。本研究介绍了一种利用 CT 扫描成像快速识别 COVID-19 的机器学习算法。该算法设计为计算机辅助检测模型,分析了 536 张 CT 图像(32x32 像素),将其分为 COVID-19 感染组和非感染组。该模型使用普雷维特滤波器和离散余弦变换对图像进行预处理,然后通过各种统计方法和定向梯度直方图(HOG)提取特征。在分析的 32 个特征中,29 个显示出高度显著性(p 值小于 0.05),能有效区分正常和异常病例。这些特征采用支持向量机(SVM)和 k-nearest neighbor(KNN)方法进行分类。灵敏度、特异性和准确性等性能指标被用来评估分类器。指标结果表明,将 KNN-1、KNN-3、KNN-5 和 SVM-Linear 分类器应用于拟议模型的 ROI 图像测试时,它们能完美区分正常和异常图像(100%)。此外,SVM-RBF 的准确率为 98.38%,表现不如其他分类器,但仍处于高性能水平。这些结果表明,医生可以利用所提出的模型作为辅助工具来检测 COVID-19。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covid-19 Detection by Machine Learning Using Chest Radiographs
The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted the importance of early detection of infections, especially when RT-PCR testing equipment is scarce. This study introduces a machine learning algorithm using CT scan imaging for rapid COVID-19 identification. The algorithm, designed as a computer-aided detection model, analyzed 536 CT images (32x32 pixels) categorized into COVID-19 infected and non-infected groups. The model preprocesses images using the Prewitt filter and discrete cosine transform, then extracts features through various statistical methods and the histogram of oriented gradients (HOG). Out of 32 analyzed features, 29 showed high significance (p-value < 0.05), effectively distinguishing normal and abnormal cases. These features were classified using support vector machine (SVM) and k-nearest neighbor (KNN) methods. Performance metrics like sensitivity, specificity, and accuracy were used to evaluate the classifiers. The results of metrics showed that the classifiers of KNN-1, KNN-3, KNN-5, and SVM-Linear could distinguish between normal and abnormal images perfectly (100%) when it was applied to the proposed model on the tested ROIs images. Also, the SVM-RBF had less performance than other classifiers with 98.38% of accuracy but was still at a high-performance level. These results indicate that physicians can utilize the proposed model as an assisted tool for detecting COVID-19.
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