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 (Institute of Electrical and Electronics Engineers Inc., 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 46900×180. High-dimensional of these HOG feature vectors is reduced to lower-dimensional with Linear Discriminant Analysis (LDA). These low-dimensional data are 180×180. 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. © 2017 IEEE.
15th IEEE International Symposium on Intelligent Systems and Informatics, SISY 2017 -- 14 September 2017 through 16 September 2017 -- Subotica -- 131846
Keywords: Artificial neural network; Hog feature extract; Linear discriminant analysis; Stomach cancer