Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification.

ABSTRACT: A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be differe

SUBMITTER: Ahmed KB 

PROVIDER: S-EPMC8224464 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature