Feature Based Automated Detection of COVID-19 from Chest X-Ray Images

Bardhan, Shawli; Roga, Sukanta

Nowadays the biggest challenge for health care is controlling the pandemic of Coronavirus disease 2019 (COVID-19). Radiological investigation combining with machine learning can serve as a standardized methodology for detecting COVID-19. Chest X-ray imaging is the most feasible radiological test for COVID-19. Machine learning-based automated classification of COVID-19 from chest X-ray images can act as an assistive method to the medical experts for accurate diagnosis of disease. Aiming at this, the study focused on developing a simplified method of X-ray image based computerized COVID-19 detection through conventional feature extraction and classification approach. The method of X-ray image based COVID-19 detection consists of only two main steps: feature extraction, and classification. In feature extraction, a total of 55 X-ray image texture features is extracted from seven different groups. Classification of COVID-19 has been performed using those extracted features through four different popularly used classifiers. The overall analysis of the study has been performed over two datasets. The Random Forest classifier generates the best accuracy of 98.6% and 98.9% for dataset 1 and 2 with the area under the curve (AUC) values 0.99 and 1 respectively. The outcome of our study provides optimal accuracy of COVID-19 classification using X-ray images compare to existing popular studies in this domain. The reliable and less-complex feature of the proposed method may serve it as a computerized X-ray image based COVID-19 detection mechanism, especially in rural areas where medical experts are not available.