DocumentCode :
380725
Title :
Multiperspective recognition applied to the computer-aided medical diagnosis - a comparative study of methods
Author :
Kurzynsk, Marek W. ; Puchala, Edward
Author_Institution :
Fac. of Electron., Wroclaw Univ. of Technol., Poland
Volume :
4
fYear :
2001
fDate :
2001
Firstpage :
3807
Abstract :
Deals with the multiperspective recognition technique applied to computer-aided decisions in medicine. For three different concepts of multiperspective classification, i.e. direct, decomposed independent and decomposed dependent approach, several decision algorithms are presented. They are: probabilistic (empirical Bayes) algorithm, nearest neighbour algorithm, fuzzy method and artificial neural network of the back propagation and counter propagation types. Proposed methods and algorithms have been applied to the computer-aided diagnosis of chronic renal failure and decisions in non-Hodgkin lymphoma. Results of experimental investigations on the real data and outcomes of the comparative analysis of the algorithms discussed are presented.
Keywords :
Bayes methods; backpropagation; decision theory; fuzzy set theory; inference mechanisms; kidney; medical diagnostic computing; object recognition; pattern classification; pattern recognition; artificial neural network; back propagation type; chronic renal failure; computer aided medical diagnosis; counter propagation type; decision algorithms; decomposed dependent approach; direct decomposed independent approach; empirical Bayes algorithm; fuzzy method; multiperspective recognition; nearest neighbour algorithm; nonHodgkin lymphoma; probabilistic algorithm; real data; Algorithm design and analysis; Artificial neural networks; Back; Computer aided diagnosis; Counting circuits; Diseases; Fuzzy neural networks; Medical diagnosis; Medical diagnostic imaging; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
ISSN :
1094-687X
Print_ISBN :
0-7803-7211-5
Type :
conf
DOI :
10.1109/IEMBS.2001.1019668
Filename :
1019668
Link To Document :
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