Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification
Samantha Denise F. Hilado*1, Laurence A. Gan Lim*1,
Raouf N. G. Naguib*2, Elmer P. Dadios*3, and Jose Maria C. Avila*4
*1Mechanical Engineering Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines
*2BIOCORE Research and Consultancy International (BIOCORE, Coventry University), Liverpool, United Kingdom
*3Manufacturing Engineering and Management Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines
*4Department of Pathology, University of the Philippines, Manila, Philippines
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.
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