Implementation of wavelets and artificial neural networks in colonic histopathological classification

College

Gokongwei College of Engineering

Department/Unit

Mechanical Engineering

Document Type

Article

Source Title

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volume

18

Issue

5

First Page

792

Last Page

797

Publication Date

9-1-2014

Abstract

Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy. © 2014, Fuji Technology Press. All rights reserved.

html

Digitial Object Identifier (DOI)

10.20965/jaciii.2014.p0792

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Mechanical Engineering

Keywords

Imaging systems in medicine; Colon (Anatomy)—Cancer—Diagnosis; Wavelets (Mathematics); Neural networks (Computer science)

Upload File

wf_no

This document is currently not available here.

Share

COinS