Abstract—In this research, we propose the image processing and classification using image extraction and data mining with ensemble learning techniques. We apply image extraction to determine the appropriate subset of the initial features to be used later by the ensemble learning for inducing an accurate model to predict cancer from the colorectal cancer histology images. Our proposed ensemble image classification method consists of three main parts: the image pre-processing part to adjust the image contrast to show the clear nucleus that can be recognized as the cause of cancer, the image extraction part to extract only important features, and finally the model creation part that generate the model to be used later as an image-based predictor. The experimental results show that the proposed method can predict the colorectal cancer from the colon images with high accuracy.
Index Terms—Image classification, image pre-processing, image extraction, ensemble learning.
Nuntawut Kaoungku and Kittisak Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon
Ratchasima 30000, Thailand (e-mail: nuntawut@sut.ac.th,
kerdpras@sut.ac.th).
Nittaya Kerdprasop is with the School of Computer Engineering and Data
and Knowledge Engineering Research Unit, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail:
nittaya@sut.ac.th).
Ratiporn Chanklan is with the Data and Knowledge Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology, Thailand. (e-mail: arc_angle@hotmail.com).
Keerachart Suksut is with the Computer Engineering Department, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand (e-mail: mikaiterng@gmail.com).
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Cite: Nuntawut Kaoungku, Kittisak Kerdprasop, Nittaya Kerdprasop, Ratiporn Chanklan, and Keerachart Suksut, "Colorectal Cancer Histology Image Classification Using Stacked Ensembles," International Journal of Future Computer and Communication vol. 8, no. 3, pp. 104-108, 2019.
