Abstract
This paper introduces a new method for the early detection of colon cancer using a combination of feature extraction based on wavelets for Fourier Transform Infrared Spectroscopy (FTIR) and classification using the Support Vector Machine (SVM). The FTIR data collected from 36 normal SD rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats was first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. The extracted feature variants were then inputted into the SVM for classification of normal, dysplasia, early carcinoma, and advanced carcinoma. Among the kernel functions the SVM used, the Poly and RBF kernels had the highest accuracy rates. The accuracy of the Poly kernel in normal, dysplasia, early carcinoma, and advanced carcinoma were 100, 97.5, 95% and 100% respectively. The accuracy of RBF kernel in normal, dysplasia, early carcinoma, and advanced carcinoma was 100, 95, 95% and 100% respectively. The results indicated that this method could effectively and easily diagnose colon cancer in its early stages.
Highlights
Colon cancer is a potent disease that is one of the major causes of mortality in both men and women
If colorectal cancer is detected at an early stage, the 5-year relative survival rate is 90%; only 37% of colorectal cancers are diagnosed at early stages [7]
Bai et al [16] reported a feature extracting method based on the dyadic wavelet transform (DWT) for Fourier Transform Infrared Spectroscopy (FTIR) cancer data analysis
Summary
Colon cancer is a potent disease that is one of the major causes of mortality in both men and women. Bai et al [16] reported a feature extracting method based on the dyadic wavelet transform (DWT) for FTIR cancer data analysis. SVM fixes the classification decision function based on structural risk minimum mistake instead of the minimum mistake of the misclassification on the training set to avoid over-fitting problem It performs binary classification problem by finding maximal margin hyperplanes in terms of a subset of the input data (support vectors) between different classes. FTIR spectroscopy, using a single bounce HATR accessory, was used to detect normal and different stage colonic cancer tissues of rates. In order to improve classification accuracy, some important features needed to be extracted in the CWT domain These features were inputted into the SVM to identify the normal, dysplasia, early carcinoma, and advanced carcinoma tissues of rates. The results showed that the new method was more efficient and much better in identification than traditional methods solely based on Fourier infrared spectrum
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