<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.</p>
</abstract>

Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.

Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to...

Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines

Publié en ligne: 17 août 2023

Pages: 163 - 169

DOI: https://doi.org/10.2478/acss-2023-0016

Mots clés
COVID-19, CXR, feature extraction, SVM, SURF

© 2023 Maher I. Rajab, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines

Publié en ligne: 17 août 2023

Pages: 163 - 169

DOI: https://doi.org/10.2478/acss-2023-0016

Mots clésCOVID-19, CXR, feature extraction, SVM, SURF

© 2023 Maher I. Rajab, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
COVID-19, CXR, feature extraction, SVM, SURF