Article: An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning.
International journal of imaging systems and technology
2021 Volume 31, Issue 2, Page(s) 499–508
Abstract: 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 ... ...
| Abstract | 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%. |
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| Language | English |
| Publishing date | 2021-03-01 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2009087-0 |
| ISSN | 1098-1098 ; 0899-9457 |
| ISSN (online) | 1098-1098 |
| ISSN | 0899-9457 |
| DOI | 10.1002/ima.22564 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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