A Expert System for Stomach Cancer Images with Artificial Neural Network by using HOG Features and Linear Discriminant Analysis: HOG_LDA_ANN - Korkmaz, Sevcan Aytac; Binol, Hamidullah; Akcicek, Aysegul; Korkmaz, Mehmet Fatih (Ieee, 2017)
In this study, normal (n), benign (b), and malign (m) stomach image cells have taken from faculty of Medicine the Firat University with Light Microscope help. Total number of stomach images are 180 which be 60 n, 60 b, and 60 m. 90 of these 180 stomach images have been used for testing purposes and 90 have used for training purposes. The histograms of oriented gradient (HOG) feature vectors have been obtained for normal, benign, and malign original stomach images. The size of these HOG feature vectors is 46900x180. High-dimensional of these HOG feature vectors is reduced to lower-dimensional with Linear Discriminant Analysis (LDA). These low-dimensional data are 180x180. These low-dimensional data are classified as normal benign and malign by artificial neural network (ANN) classification. Thus, HOG_LDA_ANN method for stomach cancer images have developed. Diagnostic accuracy of classification results with this method has found as 88.9%. According to the other methods, this result has higher accuracy result. And this result has found in a shorter time.
15th IEEE International Symposium on Intelligent Systems and Informatics (SISY) -- SEP 14-16, 2017 -- Subotica, SERBIA
Keywords: hog feature extract; Stomach cancer; linear discriminant analysis; artificial neural network