기계학습 기반 췌장 종양 분류에서 프랙탈 특징의 유효성 평가Evaluation of the Effect of using Fractal Feature on Machine learning based Pancreatic Tumor Classification
- Other Titles
- Evaluation of the Effect of using Fractal Feature on Machine learning based Pancreatic Tumor Classification
- Authors
- 오석; 김영재; 김광기
- Issue Date
- Dec-2021
- Publisher
- 한국멀티미디어학회
- Keywords
- Radiomics; Fractal Dimension; Hurst Exponent; Support Vector Machine
- Citation
- 멀티미디어학회논문지, v.24, no.12, pp.1614 - 1623
- Journal Title
- 멀티미디어학회논문지
- Volume
- 24
- Number
- 12
- Start Page
- 1614
- End Page
- 1623
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83106
- ISSN
- 1229-7771
- Abstract
- In this paper, the purpose is evaluation of the effect of using fractal feature in machine learning based pancreatic tumor classification. We used the data that Pancreas CT series 469 case including 1995 slice of benign and 1772 slice of malignant. Feature selection is implemented from 109 feature to 7 feature by Lasso regularization. In Fractal feature, fractal dimension is obtained by box-counting method, and hurst coefficient is calculated range data of pixel value in ROI. As a result, there were significant differences in both benign and malignancies tumor. Additionally, we compared the classification performance between model without fractal feature and model with fractal feature by using support vector machine. The train model with fractal feature showed statistically significant performance in comparison with train model without fractal feature.
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