Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

被引：7

作者：

Hilado, Samantha Denise F. ^{[1
]}

Lim, Laurence A. Gan ^{[1
]}

Naguib, Raouf N. G. ^{[2
]}

Dadios, Elmer P. ^{[3
]}

Avila, Jose Maria C. ^{[4
]}

机构：

[1] De La Salle Univ, Mech Engn Dept, 2401 Taft Ave, Manila 1004, Philippines

[2] Coventry Univ, BIOCORE Res & Consultancy Int BIOCORE, Liverpool, Merseyside, England

[3] De La Salle Univ, Mfg Engn & Management Dept, Manila 1004, Philippines

[4] Univ Philippines, Dept Pathol, Manila, Philippines

来源：

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2014年 / 18卷 / 05期

关键词：

colon cancer; medical image analysis; wavelet transform; artificial neural networks;

D O I：

10.20965/jaciii.2014.p0792

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：792 / 797

页数：6

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