Doctoral Consortium
:
Detecting Changes in LN-18 glial cell morphology using an SVM: A Supervised Machine Learning Approach to Cell Image Classification
Applicant
Event Type
Doctoral Consortium
TimeTuesday, September 153:45pm - 4:30pm
LocationPeter Freeman Room
DescriptionIn cell-based research, the process of visually monitoring cell cultures generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. Most of these experiments seek to monitor changes in cell morphology due to the addition of treatment. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, this project sought to create an image classification framework that employs machine learning to detect different LN-18 glial morphology and quantify large microscopic datasets. But, creating an image classification framework can be challenging for cell-based data. The research explores the image classification pipeline for creating a framework suitable for cell-based data.