Classification of Lung Cancer Cells using deep learning algorithms
Principal Investigator
Name
Chris Chatwin
Degrees
Ph.D.
Institution
University of Sussex
Position Title
Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-614
Initial CDAS Request Approval
Dec 6, 2019
Title
Classification of Lung Cancer Cells using deep learning algorithms
Summary
Test the classification performance of our already trained deep learning algorithms on the NLST Dataset, more specifically on the Lung cancer dataset.
Our deep learning algorithms are trained internally within the institution and the NLST dataset will be used to evaluate our findings as it doesn't include a manual segmentation of the cancer cells.
We will first define manually the cancer area on the images and then run our trained algorithm to observe their prediction performance on the NLST Dataset
Our deep learning algorithms are trained internally within the institution and the NLST dataset will be used to evaluate our findings as it doesn't include a manual segmentation of the cancer cells.
We will first define manually the cancer area on the images and then run our trained algorithm to observe their prediction performance on the NLST Dataset
Aims
- Classification of cancer stage using Deep Learning algorithms on cancer cells images
- Evaluation of Deep Learning algorithms
- Performance evaluation on external data
Collaborators
Pr. Chris Chatwin, University of Sussex
Pr. Chris Young, University of Sussex
Pr. Phil Birch, University of Sussex
Mr Sebastien Richoz, University of Sussex