Automated Triage Of Covid-19 From Various Lung Abnormalities Using Chest Ct Features

Abstract

The outbreak of COVID-19 has led to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

Design and development of frameworks for CPU verification efficiency improvement

“…From this, it can be said that the use of automated triage in the project of functional verification bug triage has contributed to a 18% increase in triaged signatures on average. This methodology greatly eases the parsing problem, and the triaged inputs that are now parsed are currently being fed to a machine learning algorithm [21], which will help further improve the debug efficiency. As part of future work, the information from input YAML files can be used to analyze simulation failure attributes.…”

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

“…[60], with an accuracy of 84 percent. Amran et al proposed [61] in 2021 utilising CNN and variations of U-Net to distinguish be-tween COVID-19 and CAP, with an accuracy of 87.9 and sensitivity of 90.7. Berrimi et al proposed [62] in 2021 for creating an automated for assisting doc-tors in COVID-19 diagnosis using X-ray and CT scan images.…”