Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.
【초록키워드】 COVID-19, SARS CoV-2, coronavirus disease, severe acute respiratory syndrome coronavirus 2, Coronavirus disease 2019, coronavirus, deep learning, Pneumonia, Intervention, Local, severe acute respiratory syndrome Coronavirus, Laboratory testing, Features, Patient, automated, chest X-ray, dataset, disease, COVID-19 patients, Thoracic, COVID-19 patient, Metrics, acute respiratory syndrome, acute respiratory syndrome coronavirus, healthy control, SARS CoV, diagnoses, healthy controls, datasets, machine learning classifiers, abnormality, approach, susceptible, country, feature, collected, composed, addition, provided, demonstrated, producing, experiments, time-consuming, baseline, 【제목키워드】 COVID-19, Chest X-ray image, approach,