1. Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification
- Author
-
Wei Wang, Xiaofeng Ling, Qingbo Li, and Jin Guang Wu
- Subjects
Pathology ,medicine.medical_specialty ,Support Vector Machine ,Article Subject ,lcsh:Medicine ,Sensitivity and Specificity ,General Biochemistry, Genetics and Molecular Biology ,Cross-validation ,Pattern Recognition, Automated ,symbols.namesake ,Stomach Neoplasms ,Spectroscopy, Fourier Transform Infrared ,Biomarkers, Tumor ,medicine ,Humans ,Diagnosis, Computer-Assisted ,Fourier transform infrared spectroscopy ,Mathematics ,Support vector machine classification ,General Immunology and Microbiology ,business.industry ,lcsh:R ,Reproducibility of Results ,Cancer ,Pattern recognition ,General Medicine ,medicine.disease ,Support vector machine ,Fourier transform ,Pattern recognition (psychology) ,symbols ,Artificial intelligence ,business ,Algorithms ,Smoothing ,Research Article - Abstract
Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.
- Published
- 2013