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Neural Network Based Prognosis for Twodimensional Tumor-like Growth

Title: Neural Network Based Prognosis for Twodimensional Tumor-like Growth.
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Name(s): Rajagopalan, Vidya, author
Foo, Simon Y., professor directing thesis
Chandra, Namas, committee member
Meyer-Baese, Anke, committee member
Meyer-Baese, Uwe, committee member
Department of Electrical and Computer Engineering, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: text
Issuance: monographic
Date Issued: 2005
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
Physical Form: online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In experimental and computational science and engineering, there are a host of practical problems where some tell-tale signs appear well before a critical event of interest surfaces, e.g., the occurrence of tumor is preceded by gradual change in cell density in the neighborhood. If these signs are detected then it is possible to prognosticate the severe conditions that are likely to occur. In this case the tumor can be predicted well before it actually appears and appropriate counter measures can be taken at an earlier stage with a higher degree of success. The effectiveness of such a system is gauged by how early the changes are detected and the accuracy of the predictions. The feasibility of using a combination of wavelets and neural networks as a prognostic tool is explored in this thesis. There are two phases to implementing a prognosis system for tumor-like growth. The first phase involves characterizing the data so that the tell-tale signs are detected accurately. The next phase involves choosing an appropriate predictive tool to accurately model the growth of the tumor. If this system is built with some learning capability then it could emulate if not surpass the acumen of an expert. Wavelet analyses of incoming signals serve as a preprocessing tool while the neural network is used as the predictor. This wavelets-neural network prognosis tool is directly applied to detect the development of a tumor, well before an expert human eye can perceive the problem. Computer simulations are performed using real and simulated data sets, and conclusions are drawn from the results.
Identifier: FSU_migr_etd-2141 (IID)
Submitted Note: A Thesis Submitted to the Department of Electrical Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Science.
Degree Awarded: Summer Semester, 2005.
Date of Defense: April 25, 2005.
Keywords: Wavelets, Neural Networks, Prediction
Bibliography Note: Includes bibliographical references.
Advisory Committee: Simon Y. Foo, Professor Directing Thesis; Namas Chandra, Committee Member; Anke Meyer-Baese, Committee Member; Uwe Meyer-Baese, Committee Member.
Subject(s): Electrical engineering
Computer engineering
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_migr_etd-2141
Owner Institution: FSU