Fuzzy neural network approach for noninvasive diagnosis of digestive diseases using wavelet comparing to classification followed by fuzzy C-mean algorithm

Author

Einalou, Zahra ; Maghooli, Keivan

Author_Institution

Sci. & Res. Branch, Dept. Biomed. Eng., Islamic Azad Univ., Tehran, Iran

fYear

2010

fDate

3-4 Nov. 2010

Firstpage

1

Lastpage

4

Abstract

In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the processing, the extracted signal in wavelet domain is registered. Genetic Algorithm (G.A) with binary chromosomes is used for feature selection to reduce the dimensions of feature space. Classification of digestive diseases was carried out by fuzzy neural network and fuzzy C-means algorithm. Eventually the two methods were compared. This structure is updatable or on the other word, by receiving a new signal the corresponding disease classification is updated in the feature domain.

Keywords

acoustic signal processing; bioacoustics; data reduction; diseases; feature extraction; fuzzy logic; genetic algorithms; medical signal processing; neural nets; patient diagnosis; signal classification; wavelet transforms; digestive disease classification; digestive disease noninvasive diagnosis; feature selection; feature space dimensionality reduction; fuzzy C-means algorithm; fuzzy neural network approach; genetic algorithm; signal classification; wavelet domain signal; wavelet transform; Accuracy; Feature extraction; Transforms; C-means algorithm; digestive disease; fuzzy neural network; wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering (ICBME), 2010 17th Iranian Conference of

Conference_Location

Isfahan

Print_ISBN

978-1-4244-7483-7

Type

conf

DOI

10.1109/ICBME.2010.5704933

Filename

5704933