Skip to Main Content
An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Detecting Lung Cancer by Applying SOTA Deep Learning Models to Chest X-Ray Images

Principal Investigator

Name
QINGZHONG LIU

Degrees
Ph.D.

Institution
Sam Houston State University

Position Title
Full Professor

Email
liu@shsu.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCOI-1024

Initial CDAS Request Approval
Aug 22, 2022

Title
Detecting Lung Cancer by Applying SOTA Deep Learning Models to Chest X-Ray Images

Summary
In this study, we aim to investigate the detection performance by applying SOTA deep learning models to detecting lung cancer chest X-ray images, which may be helpful for early and convenient diagnosis of lung cancer.

We will first examine the performance of SOTA deep learning models to chest X-Ray images and then transfer the learning to other types of cancer data, and see if the latest AI techniques and models may be used for the diagnosis of the cancers.

Aims

Convenient diagnosis of lung cancer by using chest X-ray images with deep learning models

Collaborators

Zhongxue Chen, Professor, Biostatistics, Indiana University at Bloomington