Khan Murtaza Ali
Department of Computer Science Umm Al-Qura University Makkah Al-Mukarramah Saudi Arabia.
Int J Imaging Syst Technol. 2021 Jun;31(2):499-508. doi: 10.1002/ima.22564. Epub 2021 Mar 1.
A type of coronavirus disease called COVID-19 is spreading all over the globe. Researchers and scientists are endeavoring to find new and effective methods to diagnose and treat this disease. This article presents an automated and fast system that identifies COVID-19 from X-ray radiographs of the chest using image processing and machine learning algorithms. Initially, the system extracts the feature descriptors from the radiographs of both healthy and COVID-19 affected patients using the speeded up robust features algorithm. Then, visual vocabulary is built by reducing the number of feature descriptors via quantization of feature space using the K-means clustering algorithm. The visual vocabulary train the support vector machine (SVM) classifier. During testing, an X-ray radiograph's visual vocabulary is sent to the trained SVM classifier to detect the absence or presence of COVID-19. The study used the dataset of 340 X-ray radiographs, 170 images of each Healthy and Positive COVID-19 class. During simulations, the dataset split into training and testing parts at various ratios. After training, the system does not require any human intervention and can process thousands of images with high precision in a few minutes. The performance of the system is measured using standard parameters of accuracy and confusion matrix. We compared the performance of the proposed SVM-based classier with the deep-learning-based convolutional neural networks (CNN). The SVM yields better results than CNN and achieves a maximum accuracy of up to 94.12%.
一种名为COVID-19的冠状病毒疾病正在全球蔓延。研究人员和科学家们正在努力寻找诊断和治疗这种疾病的新的有效方法。本文提出了一种自动化快速系统，该系统利用图像处理和机器学习算法从胸部X光片中识别COVID-19。最初，该系统使用加速鲁棒特征算法从健康患者和感染COVID-19患者的X光片中提取特征描述符。然后，通过使用K均值聚类算法对特征空间进行量化来减少特征描述符的数量，从而构建视觉词袋。视觉词袋用于训练支持向量机（SVM）分类器。在测试过程中，将一张X光片的视觉词袋发送到经过训练的SVM分类器中，以检测是否存在COVID-19。该研究使用了包含340张X光片的数据集，健康和COVID-19阳性类别各有170张图像。在模拟过程中，数据集以不同比例分为训练和测试部分。训练后，该系统无需任何人工干预，并且可以在几分钟内高精度地处理数千张图像。系统的性能使用准确率和混淆矩阵等标准参数进行衡量。我们将所提出的基于SVM的分类器的性能与基于深度学习的卷积神经网络（CNN）进行了比较。SVM比CNN产生了更好的结果，最高准确率达到了94.12%。
