Skip to Main Content
An official website of the United States government
Upcoming Login Change: On or about August 24, CDAS will begin using the NIH Researcher Auth Service (RAS) to manage our existing login options. When logging into CDAS, you will be taken to RAS, where you can select your preferred login provider to access CDAS.

Deep learning for early diagnosis of lung cancer using low-dose CT scans

Principal Investigator

Name
Safak Yakti

Degrees
Ph.D. Candidate

Institution
Binghamton University

Position Title
Graduate Research Associate

Email
syakti1@binghamton.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-398

Initial CDAS Request Approval
Apr 9, 2018

Title
Deep learning for early diagnosis of lung cancer using low-dose CT scans

Summary
Objective of this project is to apply deep learning on low-dose CT scans to aid in the interpretation of the scans to reduce possible human errors.

Aims

-To create a deep learning model that can correctly diagnose lung cancer from reading of CT scans
-To evaluate the performance of deep learning model compared to Computer-aided detection (CAD) algorithms

Collaborators

Dr. Mohammad T. Khasawneh (Binghamton University)