Classification of histopathological images using convolutional neural network
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this work, classification of cellular structures in the high resolutional histopathological images and the discrimination of cellular and non-cellular structures have been investigated. The cell classification is a very exhaustive and time-consuming process for pathologists in medicine. The development of digital imaging in histopathology has enabled the generation of reasonable and effective solutions to this problem. Morever, the classification of digital data provides easier analysis of cell structures in histopathological data. Convolutional neural network (CNN), constituting the main theme of this study, has been proposed with different spatial window sizes in RGB color spaces. Hence, to improve the accuracies of classification results obtained by supervised learning methods, spatial information must also be considered. So, spatial dependencies of cell and non-cell pixels can be evaluated within different pixel neighborhoods in this study. In the experiments, the CNN performs superior than other pixel classification methods including SVM and k-Nearest Neighbour (k-NN). At the end of this paper, several possible directions for future research are also proposed. © 2014 IEEE.
Açıklama
et al.;GENOPLOLE;Informatics, Biology Integrative and Complex Systems Laboratory (IBISC);Institute of Technologie at University of Evry Val d'Essonne (IUT);Mutual General of National Education (MGEN);University of Evry Val d'Essonne (UEVE)
4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014 -- 14 October 2014 through 17 October 2014 -- -- 109982
4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014 -- 14 October 2014 through 17 October 2014 -- -- 109982
Anahtar Kelimeler
Classification; Convolutional Neural Networks; Histopathological Images; Image Processing, Cells; Cellular Automata; Classification (Of Information); Convolution; Cytology; Image Classification; Image Processing; Nearest Neighbor Search; Neural Networks; Pixels; Classification Results; Convolutional Neural Network; Histopathological Images; K Nearest Neighbours (K-Nn); Pixel Classification; Spatial Dependencies; Spatial Informations; Supervised Learning Methods; Support Vector Machines
Kaynak
2014 4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014
WoS Q Değeri
Scopus Q Değeri
N/A
