Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

Hilado, SDF and Gan Lim, LA and Naguib, RNG and Dadios, EP and Avila, JMC (2014) Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 18 (5). pp. 792-797. ISSN 1343-0130

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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.

Item Type:	Article
Keywords:	colon cancer, medical image analysis, wavelet transform, artificial neural networks
Faculty / Department:	Faculty of Human and Digital Sciences > Mathematics and Computer Science
Depositing User:	Raouf Naguib
Date Deposited:	08 Jul 2016 11:42
Last Modified:	12 Jan 2018 13:28
URI:	https://hira.hope.ac.uk/id/eprint/1400

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