일반엑스선검사 교육용 시뮬레이터 개발을 위한 기계학습 분류모델 비교

• 영문명: Comparison of Machine Learning Classification Models for the Development of Simulators for General X-ray Examination Education
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 이인자(In-Ja Lee) 박채연(Chae-Yeon Park) 이준호(Jun-Ho Lee)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제45권 제2호, 111~116쪽, 전체 6쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2022.04.30

영문 초록

In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network( NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

일반엑스선검사 교육 시뮬레이터 기계학습 방사선학과 재학생 General X-ray Examination Education Simulator Machine Learning Radiological Technology Student