Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

ABSTRACT:

Background

The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas.

Methods

The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female an

SUBMITTER: Alvarez-Rodriguez L 

PROVIDER: S-EPMC9046709 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature