Identification of cancerous microscopic colonic images using neural networks
College
Gokongwei College of Engineering
Department/Unit
Mechanical Engineering
Document Type
Conference Proceeding
Source Title
Osaka University - De La Salle University Workshop 2008
Publication Date
2008
Abstract
Research has been undertaken over the past two decades in an effort to automate cancer diagnosis. Investigations in the classification of microscopic images of colonic mucosa have shown that textural features derived from grey-level co-occurrence matrices (GLCMs) are very useful. In this paper, the results of applying multi-layer perception (MLP) with back propagation learning in classifying microscopic colonic images are presented. Prior to the application of the MLP, the images were classified by a human expert according to three (3) categories, namely: normal, adenomatous polyp, and adenocarcinoma or cancerous. The images were sorted in order to produce 25 images for each category, totalling 75 images in all. Fifteen (15) images from each classification were used in the training of the network while the remaining 10 images were subsequently used for the validation and testing of the trained neural network.
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Recommended Citation
Gan Lim, L. A., Naguib, R. G., Dadios, E. P., & Avila, J. C. (2008). Identification of cancerous microscopic colonic images using neural networks. Osaka University - De La Salle University Workshop 2008 Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/5860
Disciplines
Bioimaging and Biomedical Optics | Computer Engineering
Keywords
Colon (Anatomy)—Cancer—Imaging; Neural networks (Computer science)
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