Identifying and Predicting Tumor and MS Disease Through MRI Data of Patients by Data Mining Tools

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Moazeni, Mehran | 2018

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 51859 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Akhavan Niaki, Taghi
Abstract:
Today with the development of technology in medical science, there is a need to develop new methods to analyze and process the medical images. Furthermore, increasing use of machines and computers to accomplish prediction goals delineates that these tools had promising results. Because of all the above, this research focuses on processing and analyzing medical images with using data mining tools in order to identify MS and tumor disease which have been ubiquitous in last decades, fast and meticulous. To do so, we introduce a new clustering algorithm based on the modularity measure of graph networks as well as a new machine learning algorithm based on Kalman filter for Tensor-based data. Besides, we propose a new hybrid algorithm for MRI segmentation and compare them with other methods
Keywords:
Data Mining ; Brain Tumor ; MS Disease ; Kalman Filters ; Magnetic Resonance Imagin (MRI) ; Clustering ; Forecasting ; Image Processing

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