Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique

Abstract

To develop an effective and objective diagnostic method for detecting the malignancy is of great importance. Considering that near-infrared (NIR) spectroscopy has many advantages such as being inexpensive and simple in sample preparation and class-modeling is a rather new strategy, the present paper investigates the feasibility of combining class-modeling technique other than classic classification and NIR spectroscopy for colorectal diagnosis. A total of 162 colorectal tissue slices were prepared and used to collect NIR spectra. A special variable importance (VI) index was defined to pick out 20 most significant variables. The Kennard-Stone (KS) algorithm was used to select representative 57 cancerous samples as the training set for building one-class model and the other samples served as the test set. The results showed that on the independent test set, it can achieve acceptable performance, i.e., the total accuracy of 95.2 %, the sensitivity of 96 %, and the specificity of 94.5 %. It indicates that the combination of NIR spectroscopy and one-class classifier is a potential tool for automatic cancer diagnosis.