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Improving the diagnosis of lung cancer based on multiparametric MRI
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Paper Abstract

In order to improve the diagnostic effect of MRI images, a multiparametric magnetic resonance imaging (MRI) based classification method was proposed in this paper. The study included 85 patients. The radiomics method was used to extract morphological and texture features, while Apparent diffusion coefficient (ADC) was used as functional feature.Three classification methods, including Linear Discriminate Analysis (LDA), Support Vector Machine (SVM) and Random Forest (RF), were used to distinguish benign and malignant of pulmonary lesions. The performance of multiparametric MRI sequences and single sequences were compared. The experimental results shown that multiparametric MRI classification with SVM classifier had best performence (AUC=0.82±0.03), indicating that multiparametric MR diagnosis has great potential.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731Z (3 January 2020); doi: 10.1117/12.2557474
Show Author Affiliations
Xinhui Wang, Beijing Jiaotong Univ. (China)
Houjin Chen, Beijing Jiaotong Univ. (China)
Yanfeng Li, Beijing Jiaotong Univ. (China)
Chen Yan, Beijing Jiaotong Univ. (China)
Yahui Peng, Beijing Jiaotong Univ. (China)
Xinchun Li, The First Affiliated Hospital of Guangzhou Medical Univ. (China)

Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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