NDLI: Detection of Lung Cancer with Breath Biomarkers Based on SVM Regression

Content Provider	IEEE Xplore Digital Library
Author	Siqi Ding Tianlin Hu Yang Shen Chun Lin Yuanqing Huang
Copyright Year	2009
Abstract	According to feature extraction of high order cumulant, a new method of detecting lung cancer is proposed applying support vector machine model to recognize the mixed Volatile Organic Compound (VOC) infrared spectrum, where the primary and secondary absorbed peaks are seriously overlapped. The number of spectrum channel of the original spectrum data is large; hence, the transmitted spectrogram is mapped to four-order cumulant space and detached from each firstly. In this simulation experiment, concentration of 19 VOCs was regressed by SVM and the result shows that the method performed well in identification. The average correct rate of component recognition is more than 95.5% when component concentration of VOC is not less than 1%. MSE and MAE were introduced to assess the performance of the method. Prediction adopting SVM and ANN is compared.
Starting Page	131
Ending Page	138
File Size	1165349
Page Count	8
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.557
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Infrared detectors Cancer detection Volatile organic compound Infrared spectra Support vector machine Spectrogram Support vector machines Volatile organic compounds Lungs Biomarkers Feature extraction High order cumulant Gas detectors
Content Type	Text
Resource Type	Article

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