Abstract
A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person’s X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.
Keywords: COVID-19; Chest X-ray; Entropy; Gray level co-occurrence matrix; Local binary pattern; Support vector machine classifier.
【저자키워드】 COVID-19, chest X-ray, Entropy, Gray level co-occurrence matrix, Local binary pattern, Support vector machine classifier., 【초록키워드】 Pneumonia, Diagnosis, Local, X-ray, sensitivity, Mass screening, Accuracy, COVID-19 diagnosis, automated, chest X-ray, Combination, RT-PCR test, Entropy, Image, Chest X-ray images, Radiologists, Chest X-ray image, Classifier, robustness, radiologist, Gray, approach, feature, detection system, IMPROVE, investigated, provided, assist, co-occurrence, LBP, 【제목키워드】 information,