Automatic detection and classification of lung nodules in low dose computed tomography (LDCT) images
Principal Investigator
Name
Elham Khoshkerdar
Degrees
Ph.D. Student
Institution
University of Science and Research,Tehran, Iran
Position Title
Ph.D. Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-393
Initial CDAS Request Approval
Mar 8, 2018
Title
Automatic detection and classification of lung nodules in low dose computed tomography (LDCT) images
Summary
Lung cancer screening program can significantly reduce mortality rate in high-risk individuals. Unfortunately, the CAD systems have a high false positive rate which affects the lung cancer screening outcomes. FPs are a main challenging issue in the case of the lung cancer screening program in the world. In this Ph.D. project, we are going to use new features and classifiers to improve the sensitivity and reduce FP rate of CAD algorithms.
Aims
1- Reducing the false positive rate of CAD algorithms
2- Increasing sensitivity of CAD algorithms
Collaborators
1- Dr. Valiallah Saba, Department of Radiology, AJA university of Medical Sciences, Tehran, Iran.
2- Dr. Saeed Setayeshi, Medical Rad Eng Dept, Amirkabir University of Technology, Tehran, Iran