Colorectal Cancer Classification using PCA and Fisherface Feature Extraction Data from Pathology Microscopic Image

by Fajri Rakhmat Umbara,Adiyasa Nurfalah,The Houw Liong
( Telkom Institute of Technology, Telkom Institute of Technology, Telkom Institute of Technology )

Date Published: 02 Dec 2013
Published In: Information Systems International Conference (ISICO)
Volume: 2013
Publisher: Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
Language: id-ID

Keywords: feature extraction,Colorectal Cancer,Fisherface,PCA,Random Tree Algorithm

Abstract

Colorectal cancer is the one of variant cancer which can kill people on this earth. World Health Organization, from their website wrote about 608,000 people can get killed every year because of it. The variant of colorectal cancer such as lymphoma and carcinoma strikes colon from the inside and outside. Lymphoma can be found in white corpuscle and attack colon through lymphocytes, whereas carcinoma can attack the outer layer of colon. Early detection is needed to decrease the number of death because of this cancer. The study about colorectal cancer is to classified lymphoma, carcinoma, and normal colon. It is doing by using 198 pathology microscopic images data from Hasan Sadikin Hospital in Bandung, Indonesia. Feature extraction using PCA and Fisherface and each generate 2, 5, 10, 50, 100 features. The study compared these two methods and using WEKA to testing the accuracy. Using 10 folds cross-validation and 3 different classifier in WEKA such as Random Tree, Multi Layer Perceptron, and Naïve Bayes, Fisherface has capability for classified colorectal cancer around 84% - 100% for accuracy. It came from almost all features. Difference result is much visible in PCA. From this result, Fisherface is better than PCA for feature extraction.


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