| Title |
Machine Learning based Gastric Cancer Computer-aided Diagnosis System using Feature Selection |
| Authors |
김윤지(Yun-ji Kim) ; 이신애(Sin-ae Lee) ; 김동현(Dong-hyun Kim) ; 채정우(Jung-woo Chae) ; 함현식(Hyun-sik Ham) ; 조현진(Hyun Chin Cho) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.1.170 |
| Keywords |
CADx; Feature selection; Gastric Endoscopy; SVM |
| Abstract |
Gastric cancer is a kind of cancer that is difficult to detect at an early stage because it has almost no symptoms at the beginning.
In this study, we propose a Computer-aided Diagnosis(CADx) system that detects gastric cancer from the endoscopy. The data set we used consist of 93 normal images and 93 gastric images. We extracted 6 features in 449 dimensions from the gastric endoscopy images and reduced them to 10 dimensions through feature selection algorithms. Algorithms that we use to dimension reduction are Pearson Correlation, Chi-Squared Test, Recursive Feature Elimination, and Model-based Feature Selection, which are provided by the Sci-kit Learn library. A method was also used to select the top 10 features with a higher number of times selected by these four algorithms. Normal images and gastric cancer images were classified using support vector machine(SVM). Recursive feature elimination algorithm has the highest performance among the five feature selection algorithms, with an accuracy of 0.92. |