Application of artificial neural networks for classification of colonoscopic images

Author

Tjoa, M.P. ; Krishnan, M. ; Yap, J. ; Swaminathan, S. ; Wang, P.

Author_Institution

BioMedical Eng. Res. Centre, Nanyang Technol. Univ., Singapore

Volume

2

fYear

2002

fDate

2002

Firstpage

227

Abstract

Computer-aided methods in colonoscopy play an important role of assisting the physician in detecting abnormalities by characterizing the features of the segmented colonoscopic images. A novel image processing approach is proposed to extract the parametric features of the colonoscopic image. A comparison between two different types of neural networks, viz., the backpropagation (BP) and the adaptive resonance theory (ART) networks, is carried out to classify the status of the colon. The preliminary results obtained by the proposed methods are encouraging.

Keywords

ART neural nets; backpropagation; biomedical optical imaging; cancer; feature extraction; image classification; medical image processing; tumours; ART networks; BP ANN; adaptive resonance theory networks; artificial neural networks; backpropagation ANN; colon abnormality detection; colon status classification; colonoscopic image classification; colonoscopy; colorectal cancer; computer-aided colonoscopy; image processing; parametric feature extraction; patient diagnosis; tumors; Application software; Artificial neural networks; Backpropagation; Colonoscopy; Feature extraction; Image processing; Image segmentation; Neural networks; Physics computing; Resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2002. APCCAS '02. 2002 Asia-Pacific Conference on

Print_ISBN

0-7803-7690-0

Type

conf

DOI

10.1109/APCCAS.2002.1115199

Filename

1115199