How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F23%3A43907332" target="_blank" >RIV/60076658:12310/23:43907332 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-023-45368-w" target="_blank" >https://www.nature.com/articles/s41598-023-45368-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-023-45368-w" target="_blank" >10.1038/s41598-023-45368-w</a>

Result language

angličtina

Original language name

How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images

Original language description

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods&apos; (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

—

UT code for WoS article

001102763800016

EID of the result in the Scopus database

—