Classification of skin cancer based on fluorescence lifetime imaging and machine learning | |
Yang, Qianqian2; Qi, Meijie2; Wu, Zhaoqing2; Liu, Lixin2,3,4; Gao, Peng2; Qu, Junle1 | |
2020 | |
会议日期 | 2020-10-11 |
会议地点 | ELECTR NETWORK |
关键词 | skin cancer fluorescence lifetime machine learning binary classification |
卷号 | 11553 |
DOI | 10.1117/12.2573851 |
英文摘要 | To evaluate the development stage of skin cancer accurately is very important for prompt treatment and clinical prognosis. In this paper, we used the FLIM system based on time-correlated single-photon counting (TCSPC) to acquire fluorescence lifetime images of skin tissues. In the cases of full sample data, three kinds of sample set partitioning methods, including bootstrapping method, hold-out method and K-fold cross-validation method, were used to divide the samples into calibration set and prediction set, respectively. Then the binary classification models for skin cancer were established based on random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) respectively. The results showed that FLIM combining with appropriate machine learning algorithms can achieve early and advanced canceration classification of skin cancer, which could provide reference for the multi-classification, clinical staging and diagnosis of skin cancer. |
产权排序 | 2 |
会议录 | OPTICS IN HEALTH CARE AND BIOMEDICAL OPTICS X |
会议录出版者 | SPIE-INT SOC OPTICAL ENGINEERING |
语种 | 英语 |
ISSN号 | 0277-786X;1996-756X |
ISBN号 | 978-1-5106-3922-5 |
WOS记录号 | WOS:000651830000030 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/94835] |
专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
通讯作者 | Liu, Lixin |
作者单位 | 1.Shenzhen Univ, Coll Phys & Optoelect Engn, Minist Educ & Guangdong Prov, Key Lab Optoelect Devices & Syst, Shenzhen 518060, Peoples R China 2.Xidian Univ, Sch Phys & Optoelect Engn, Xian 710071, Peoples R China 3.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China 4.CAS Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Yang, Qianqian,Qi, Meijie,Wu, Zhaoqing,et al. Classification of skin cancer based on fluorescence lifetime imaging and machine learning[C]. 见:. ELECTR NETWORK. 2020-10-11. |
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