Wso-Caps: Diagnosis Of Lung Infection From Low And Ultra-Lowdose CT Scans Using Capsule Networks And Windowsetting Optimization

Abstract

The automatic diagnosis of lung infections using chest computed tomography (CT) scans has been recently obtained remarkable significance, particularly during the COVID-19 pandemic that the early diagnosis of the disease is of utmost importance. In addition, infection diagnosis is the main building block of most automated diagnostic/prognostic frameworks. Recently, due to the devastating effects of the radiation on the body caused by the CT scan, there has been a surge in acquiring low and ultra-low-dose CT scans instead of the standard scans. Such CT scans, however, suffer from a high noise level which makes them difficult and time-consuming to interpret even by expert radiologists. In addition, some abnormalities are only visible using specific window settings on the radiologists’ monitor. Currently, manual adjustment of the windowing settings is the common approach to analyze such low-quality images. In this paper, we propose an automated framework based on the Capsule Networks, referred to as the “WSO-CAPS”, to detect slices demonstrating infection using low and ultra-low-dose chest CT scans. The WSOCAPS framework is equipped with a Window Setting Optimization (WSO) mechanism to automatically identify the best window setting parameters to resemble the radiologists’ efforts. The experimental results on our in-house dataset show that the WSO-CAPS enhances the capability of the Capsule Network and its counterparts to identify slices demonstrating infection. The WSO-CAPS achieves the accuracy of 92.0%, sensitivity of 90.3%, and specificity of 93.3%. We believe that the proposed WSO-CAPS has a high potential to be further utilized in future frameworks that are working with CT scans, particularly the ones which utilize an infection diagnosis step in their pipeline.