Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2023
Committee Chair or Co-Chairs
Mostafa Zahed
Committee Members
Robert Price, JeanMarie Hendrickson
Abstract
Over the years, cancer has increasingly become a global health problem [12]. For successful treatment, early detection and diagnosis is critical. Radiomics is the use of CT, PET, MRI or Ultrasound imaging as input data, extracting features from image-based data, and then using machine learning for quantitative analysis and disease prediction [23, 14, 19, 1]. Feature reduction is critical as most quantitative features can have unnecessary redundant characteristics. The objective of this research is to use machine learning techniques in reducing the number of dimensions, thereby rendering the data manageable. Radiomics steps include Imaging, segmentation, feature extraction, and analysis. For this research, a large-scale CT data for Lung cancer diagnosis collected by scholars from Medical University in China is used to illustrate the dimension reduction techniques via R, SAS, and Python softwares. The proposed reduction and analysis techniques were PCA, Clustering, and Manifold-based algorithms. The results indicated the texture-based features
Document Type
Thesis - embargo
Recommended Citation
Kireta, Janet, "Unsupervised Dimension Reduction Techniques for Lung Diagnosis using Radiomics" (2023). Electronic Theses and Dissertations. Paper 4198. https://dc.etsu.edu/etd/4198
Copyright
Copyright by the authors.