Automatic Lung Abnormality Detection and Classification based on Chest X-rays

Principal Investigator

Name
Lin Yang

Degrees
Ph.D.

Institution
University of Florida

Position Title
Associate Professor

Email
lin.yang@bme.ufl.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-370

Initial CDAS Request Approval
Jun 19, 2018

Title
Automatic Lung Abnormality Detection and Classification based on Chest X-rays

Summary
Chest X-rays are the most commonly used procedures for diagnosing lung diseases. However, to accurately and automatically detect abnormal lung regions and their subtypes is still a challenge. To address this issue, we aim to propose an efficient and accurate framework for lung abnormality detection and classification using deep learning techniques.

Aims

-Develop computer-aided techniques to perform lung abnormality detection and classification through convolutional neural networks
-Benchmark against existing lung abnormalities detection and classification methods

Collaborators

University of Florida