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위 내시경 영상에서의 비정상 분류를 위한 머신러닝 기반 컴퓨터 보조 진단 시스템
이신애(Sin-ae Lee),조현진(HyunChin Cho),조현종(Hyun-chong Cho) 대한전기학회 2020 전기학회논문지 Vol.69 No.1
Gastric cancer is the most common cancer and has been the number one incidence since 1999 in Korea(as of 2016). Gastrointestinal symptoms and functional gastrointestinal disorders comprise a large proportion of primary care and gastroenterology practice. We propose a Computer-aided Diagnosis (CADx) system that analyzing the traditional gastroscope images and help the medical experts improve the accuracy of medical diagnosis. The data set we used consists of 400 normal images and 285 abnormal images from 103 volunteers. We also extracted four color features and two texture features from each image. The Least Square Support Vector Machine(LS-SVM) classifier was used for normal and abnormal classification. LS-SVM finds the solution by solving a set of linear equations instead of a convex quadratic programming problem for classical SVMs. The AUC value was 0.85, which is 0.02 higher than that of normal SVM.
컴퓨터 보조 진단 시스템 성능 개선을 위한 새로운 이미지 증대 기법
이신애(Sin-ae Lee),조현종(Hyun-chong Cho) 대한전기학회 2021 전기학회논문지 Vol.70 No.1
Gastric cancer is the largest percentage of cancer cases in Korea. A precise way to find the occurrence of gastrointestinal diseases is through gastroscopy by a trained diagnostic physician. Computer-aided diagnosis (CADx) system helps improve the reliability and speed of diagnosis. The CADx system has developed with deep learning, which is data dependent. However, medical image data is labor intensive and time consuming, making it difficult for large data sets to be formed. To solve this problem, it is important to apply augmentation techniques. In this paper, we propose an augmentation method which is suitable for the data. A basic classification model was made by leaning a data set consisting of the original images. Each of the 14 augment technique-applied data set was input into the generated model and the f1-score values were compared. The f1-score of the highest performance among the proposed methods, was 0.9221, with an increase of about 0.085.
특징 선택을 활용한 머신 러닝 기반의 위암 컴퓨터 보조 진단 시스템
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) 대한전기학회 2020 전기학회논문지 Vol.69 No.1
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.