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Deep Learning for Lung Cancer Detection

Principal Investigator
Name
Ryan Sherman
Degrees
B.S.
Institution
Deep Analytics
Position Title
Machine Learning Engineer
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-341
Initial CDAS Request Approval
Sep 27, 2017
Title
Deep Learning for Lung Cancer Detection
Summary
Using deep learning techniques such as convolutional neural networks, segmentation algorithms like UNet, gradient boosting algorithms, as well as computer vision algorithms, lung cancer can be diagnosed given a CT scan. Lung cancer is a deadly and difficult cancer to detect, so deep learning fits the problem very well as it can be given lots of data, learn, and perform well. Using machine learning libraries, like Tensorflow or Keras, deep learning software can be built to diagnose lung cancer.
Aims

The goal of this project is to save lives, time, and money. Lung cancer takes the lives of around 1.7 million people every year, and early detection of lung cancer doubles the chance of survival. Also, radiologists have limited time to look at CT scans and diagnose patients, making it easy for misdiagnoses. By building intelligent software that can diagnose cancer in the blink of an eye, it will save lots of time for radiologists. Additionally, it is very costly to visit several doctors and radiologists to get a diagnosis, so this project will ensure that the patient will only have to get one diagnosis without having to worry about visiting multiple doctors to make sure the diagnosis is correct. This project also seeks to increase accessibility of this technology to everyone, so they have a greater chance at surviving lung cancer, and I hope the project will inspire others to take part in the fight against cancer.

Collaborators

Ryan Sherman
Deep Analytics