Event Title

Use of Machine Learning for Non-Invasive Identification of Tumors

Start Date

5-6-2019 10:15 AM

Description

Under Utah Valley University (UVU) Physics Department the Center for Imaging and Biophotonic Experiments Advancing Medicine. (CIBEAM) is focused on techniques for early cancer detection. The undergraduate based research project with the instruction of Dr. Vern Hart has developed a methodology using the scattering profile of a laser through a specimen to classify between different cancer cell types with machine learning algorithms. This technique works by passing a near-infrared (NIR) laser through a cell monolayer and collecting the scattering profile. Thousands of images are collected for the purpose of training a artificially intelligent convolution neural network as well as to build a testing dataset for verification. Additionally, this group has focused on involving many students in various research thanks to the many facets required for this project such as lasers, fabrication, 3D printing, cell growth, and machine learning.

Comments

Poster Session

This document is currently not available here.

Share

COinS
 
May 6th, 10:15 AM

Use of Machine Learning for Non-Invasive Identification of Tumors

Under Utah Valley University (UVU) Physics Department the Center for Imaging and Biophotonic Experiments Advancing Medicine. (CIBEAM) is focused on techniques for early cancer detection. The undergraduate based research project with the instruction of Dr. Vern Hart has developed a methodology using the scattering profile of a laser through a specimen to classify between different cancer cell types with machine learning algorithms. This technique works by passing a near-infrared (NIR) laser through a cell monolayer and collecting the scattering profile. Thousands of images are collected for the purpose of training a artificially intelligent convolution neural network as well as to build a testing dataset for verification. Additionally, this group has focused on involving many students in various research thanks to the many facets required for this project such as lasers, fabrication, 3D printing, cell growth, and machine learning.