K-Nearest Neighbor for colon cancer identification
Abstract
Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack humans colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this study is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal/healthy colon condition. This system will classify colon cancer starts from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resized, convert RGB image to grayscale, edge detection, and last histogram equalization. Tests will be done by trying some K-NN input parameter settings. The result of this study is an image processing system that can detect and classify type of colon cancer with high accuracy and low computation time.
- Publication:
-
Journal of Physics Conference Series
- Pub Date:
- November 2019
- DOI:
- 10.1088/1742-6596/1367/1/012023
- Bibcode:
- 2019JPhCS1367a2023K