A New Application Based on GPLVM, LMNN, and NCA for Early Detection of the Stomach Cancer

Abstract

In this article, speeded-up robust features (SURF) for each image have been calculated. Discrete Fourier transform (DFT) method has been applied to these SURF. High dimensions of these SURF-DFT feature vectors are reduced to low dimensions with large-margin nearest neighbor (LMNN), Gaussian process latent variable models (GPLVM), and neighborhood component analysis (NCA). When size reduction process was done, effect on the GPLVM, LMNN, and NCA of the 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 feature numbers has been examined. These features are classified by naive Bayes (NB) classifier. Thus, SURF_DFT_GPLVM_NB, SURF_DFT_NCA_NB, and SURF_DFT_LMNN_NB methods for gastric histopathological images have been developed. Classification results obtained with these methods have been compared. According to the obtained results, the highest classification result was obtained as 90.24% by using 4 features by SURF_DFT_GPLVM_NB method for second group images.