Implementation of wavelets and artificial neural networks in colonic histopathological classification

• Main Authors: Hilado, Samantha Denise F., Gan Lim, Laurence A., Naguib, Raouf N. G., Dadios, Elmer P., Avila, Jose Maria C.
• Format: text
• Published: Animo Repository 2014
• Subjects: Imaging systems in medicine; Colon (Anatomy)—Cancer—Diagnosis; Wavelets (Mathematics); Neural networks (Computer science); Analytical, Diagnostic and Therapeutic Techniques and Equipment; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1928
• Institution: De La Salle University

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.