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Title:Exploring image segmentation methods to segment tumors by training over a dataset marked by skilled professionals
Author(s):Somani, Varun
Contributor(s):Forsyth, D. A.
Subject(s):image segmentation
machine learning
lung CT
supervised learning
unsupervised learning
Abstract:This research uses chest CT scan images of lung cancer patients to examine current methods in image segmentation in the context of tumor segmentation. Potential benefits of the research include faster processing and detection time for patients as well as allowing doctors to rapidly proceed with the requisite procedures. We use both supervised and unsupervised methods to segment images. In terms of supervised methods, we use neural networks and SVMs with various “kernel tricks.” In terms of unsupervised methods, we use K-means clustering and Otsu’s method. Neural network gave the best result while other methods tended to have inferior performance. The results suggest that there is a possibility of further developing neural networks to conclusively solve the problem.
Issue Date:2017-12
Date Available in IDEALS:2017-08-28

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