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Role of Image Segmentation Methods in Increasing the Efficiency and Accuracy of Neural Networks in Cancer Detection

Principal Investigator
Name
Zong Zhang
Degrees
International Baccalaureate Diploma
Institution
N/A
Position Title
Student, Intern
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-335
Initial CDAS Request Approval
Jul 28, 2017
Title
Role of Image Segmentation Methods in Increasing the Efficiency and Accuracy of Neural Networks in Cancer Detection
Summary
In previous studies, image cropping and segmentation have shown to greatly aid the creation of more accurate and perceptive neural networks. In this study, different image segmentation algorithms will be used in image prepossessing, and the effectiveness of each method will be assessed. Some methods include Chan-vese segmentation, K-means, region growing, edge detection, and so on. After the images are processed, they will be inputted into convolutional neural networks (both dense and conventional) and the logloss of the neural network over time will be an indicator for the success of both the network and the mode of image segmentation.
Aims

- Identify the most successful method of image segmentation for the NLST dataset
-Creation of a neural network with a high degree of accuracy in differentiating between high risk cancer patients and healthy patients.

Collaborators

Chen Xu, Senior at Interlake High School