Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Classification of histopathologic images and identification of cancerous areas is quite challenging due to image background complexity and resolution. The difference between normal tissue and cancerous tissue is very small in some cases. So, the features of the tissue patches in the image have key importance for automatic classification. Using only one feature or using a few features leads to poor classification results because of the small difference between the textures. In this study, the classification results are compared using different feature extraction algorithms that can extract various features from histopathological image texture. For this study, GLCM, LBP, LBGLCM, GLRLM and SFTA algorithms which are successful feature extraction algorithms have been chosen. The features obtained from these methods are classified with SVM, KNN, LDA and Boosted Tree classifiers. The most successful feature extraction algorithm for histopathological images is determined and the most successful classification algorithm is determined. © 2018 The Authors. Published by Elsevier Ltd.
Açıklama
2018 International Conference on Computational Intelligence and Data Science, ICCIDS 2018 -- 7 April 2018 through 8 April 2018 -- 137053
Anahtar Kelimeler
feature extraction, GLCM, GLRLM, histopathological image, KNN, LBGLCM, LBP, LDA, SFTA, SVM
Kaynak
Procedia Computer Science
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
132
