CovidAID: COVID-19 Detection Using Chest X-Ray

TL;DR

CovidAID is an AI-based tool that uses chest X-ray images to rapidly identify potential COVID-19 cases, aiding in prioritizing testing and resource allocation especially where testing kits are limited.

Contribution

The paper introduces CovidAID, a novel deep neural network model that outperforms previous models in COVID-19 detection from chest X-rays, with high accuracy and sensitivity.

Findings

Achieved 90.5% accuracy on the covid-chestxray dataset.

Attained 100% sensitivity for COVID-19 detection.

Significantly improved upon Covid-Net results.

Abstract

The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the samples either. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This may be useful in an inpatient setting where the present systems are struggling to decide whether to keep the patient in the ward along with other patients or isolate them in COVID-19 areas. It would also help in identifying patients with high likelihood of COVID with a false negative RT-PCR who would need repeat testing. Further, we propose the use of modern AI techniques to detect the COVID-19 patients using X-Ray images in an automated manner, particularly in settings where radiologists are not available, and help make the proposed testing technology scalable. We present CovidAID: COVID-19 AI Detector, a novel deep neural network based model to triage patients for appropriate testing. On the publicly available covid-chestxray-dataset [2], our model gives 90.5% accuracy with 100% sensitivity (recall) for the COVID-19 infection. We significantly improve upon the results of Covid-Net [10] on the same dataset.