A hierarchical fine-grained classification approach for COVID-19 severity assessment based on CT images

Abstract

The Coronavirus Disease 2019 (COVID-19) is still an ongoing health issue. Appropriate treatment is important for the recovery of COVID-19 patients. At present, numerous deep learning (DL) methods have been applied to the classification of COVID-19 based on computerized tomography (CT) images. However, most of these methods directly extract visual features from the entire CT image, leading to the inclusion of irrelevant background information and making it hard to extract critical features about lesions. Thus, we propose a hierarchical fine-grained classification approach for automatic severity assessment of COVID-19 patients. The CT images are analyzed from coarse to fine in two stages, to focus on the critical features gradually. In the first stage, we detect the lesion regions from the CT image utilizing deep learning based model. In the second stage, we extract critical features from lesion regions and make a severity grading by fine-grained classification model. The experimental results show that the proposed method achieves the classification results of 95.04%, 93.98%, 93.46% and 93.72% in the metrics of accuracy, precision, recall and F1-Score, respectively.