Uloga umjetne neuronske mreže u detekciji abnormalnosti u funkciji rada pluća
Journal
Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva
Date Issued
2009-12-24
Author(s)
Mircheska, Aneta
Stoleski, Sasho
Abstract
An artificial neural network is a system based on the operation of biological neural networks, in other words,
it is an emulation of the biological neural system. The objective of this study is to compare the performance of two
different versions of neural network ART algorithms such as Fuzzy ART vs. ARTFC methods used for classification of
pulmonary function, detecting restrictive, obstructive and normal patterns of respiratory abnormalities by means of
each of the neural networks, as well as the data gathered from spirometry. The spirometry data were obtained from
150 patients by standard acquisition protocol, 100 subjects used for training and 50 subjects for testing, respectively.
The results showed that the standard Fuzzy ART grows faster than ARTFC, which successfully solves the category
proliferation problem.
it is an emulation of the biological neural system. The objective of this study is to compare the performance of two
different versions of neural network ART algorithms such as Fuzzy ART vs. ARTFC methods used for classification of
pulmonary function, detecting restrictive, obstructive and normal patterns of respiratory abnormalities by means of
each of the neural networks, as well as the data gathered from spirometry. The spirometry data were obtained from
150 patients by standard acquisition protocol, 100 subjects used for training and 50 subjects for testing, respectively.
The results showed that the standard Fuzzy ART grows faster than ARTFC, which successfully solves the category
proliferation problem.
Subjects
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