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DEEP LEARNING USING CHEST COMPUTED TOMOGRAPHY FOR THE DETECTION AND DIAGNOSIS OF LUNG CANCER

Principal Investigator
Name
Jeremy Burt
Degrees
M.D.
Institution
Medical University of South Carolina
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-709
Initial CDAS Request Approval
Sep 12, 2020
Title
DEEP LEARNING USING CHEST COMPUTED TOMOGRAPHY FOR THE DETECTION AND DIAGNOSIS OF LUNG CANCER
Summary
Develop learning algorithms using the latest artificial intelligence methods to aid in lung cancer detection and diagnosis based on textural features in contrast-enhanced computed tomography scans. We then plan to assess the algorithm’s accuracy and performance on a new dataset compiled from patients at the Medical University of South Carolina (MUSC).
Aims

- Further develop algorithms from the 2017 Kaggle competition
- We want to use these algorithms to create our own to detect lung nodules, then segment said lung nodules.
- After segmentation the software characterizes the lung nodule.
- After characterization, the software then tells the user the likelihood of the nodule being cancerous.
- We then want to test this software with MUSC CT scans.

Collaborators

Jeremy Burt; Medical University of South Carolina (MUSC)
Brian Dean; Clemson University
John Lineberger; Clemson University
Matthew Turner; Medical University of South Carolina (MUSC)
Matthew Davis; Medical University of South Carolina (MUSC)
Rachel Mcneely; Medical University of South Carolina (MUSC)
Vincent Giovagnoli; Medical University of South Carolina (MUSC)
Grace Neil; Medical University of South Carolina (MUSC)
William Dennis; Medical University of South Carolina (MUSC)
Madison Kocher; Medical University of South Carolina (MUSC)
Jeffrey Waltz; Medical University of South Carolina (MUSC)
Dhiraj Baruah; Medical University of South Carolina (MUSC)
Anh Phan; Medical University of South Carolina (MUSC)
Nayana Somayaji; Medical University of South Carolina (MUSC)
Basel Yacoub; Medical University of South Carolina (MUSC)