Anthony, Denis (1991) The use of artificial neural networks in classifying lung scintigrams. PhD thesis, University of Warwick.

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Abstract

An introduction to nuclear medical imaging and artificial neural networks (ANNs)
is first given.
Lung scintigrams are classified using ANNs in this study. Initial experiments using
raw data are first reported. These networks did not produce suitable outputs, and a data
compression method was next employed to present an orthogonal data input set containing
the largest amount of information possible. This gave some encouraging results, but
was neither sensitive nor accurate enough for clinical use.
A set of experiments was performed to give local information on small windows of
scintigram images. By this method areas of abnormality could be sent into a subsequent
classification network to diagnose the cause of the defect. This automatic method of
detecting potential defects did not work, though the networks explored were found to act
as smoothing filters and edge detectors.
Network design was investigated using genetic algorithms (GAs). The networks
evolved had low connectivity but reduced error and faster convergence than fully connected
networks. Subsequent simulations showed that randomly partially connected networks
performed as well as GA designed ones.
Dynamic parameter tuning was explored in an attempt to produce faster convergence,
but the previous good results of other workers could not be replicated.
Classification of scintigrams using manually delineated regions of interest was
explored as inputs to ANNs, both in raw state and as principal components (PCs). Neither
representation was shown to be effective on test data.

Item Type:	Thesis (PhD)
Subjects:	R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Library of Congress Subject Headings (LCSH):	Radioisotope scanning , Radioisotope scanning -- Equipment and supplies , Neural networks (Computer science) , Lungs -- Diseases -- Diagnosis
Official Date:	February 1991
Institution:	University of Warwick
Theses Department:	Department of Chemistry
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hines, Evor, 1957-
Extent:	2 volumes in 1
Language:	eng

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