Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs

TL;DR

This paper evaluates popular CNN architectures for COVID-19 detection from chest X-rays, identifies issues in current models, and proposes improved training methodologies for more reliable results.

Contribution

It critically assesses existing models for COVID-19 detection in radiographs and introduces methodologies to enhance their reliability and performance.

Findings

Identified limitations in current CNN models' performance.

Proposed training strategies improve model reliability.

Provided open-source code and models for further research.

Abstract

Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases that may plague a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed practicing radiologists. Amidst the COVID-19 pandemic, researchers have explored and proposed the use of said deep models to detect COVID-19 infections from radiographs as a possible way to help ease the strain on medical resources. In this study, we train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions, find issues that discount the impressive model performances proposed by contemporary studies on this subject, and propose methodologies to train models that yield more reliable results.. Code, scripts, pre-trained models, and visualizations are available at https://github.com/nalbert/COVID-detection-from-radiographs.