Classification of histopathological images using convolutional neural network

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dc.authorscopusid26657939200
dc.authorscopusid8362224100
dc.contributor.authorHatipoglu N.
dc.contributor.authorBilgin G.
dc.date.accessioned2024-06-12T10:25:23Z
dc.date.available2024-06-12T10:25:23Z
dc.date.issued2015
dc.descriptionet 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)en_US
dc.description4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014 -- 14 October 2014 through 17 October 2014 -- -- 109982en_US
dc.description.abstractIn 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.en_US
dc.identifier.doi10.1109/IPTA.2014.7001976
dc.identifier.isbn9.78148E+12
dc.identifier.scopus2-s2.0-84921733346en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/IPTA.2014.7001976
dc.identifier.urihttps://hdl.handle.net/20.500.14551/16311
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2014 4th International Conference on Image Processing Theory, Tools and Applications, IPTA 2014en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassification; Convolutional Neural Networks; Histopathological Images; Image Processingen_US
dc.subjectCells; 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 Machinesen_US
dc.titleClassification of histopathological images using convolutional neural networken_US
dc.typeConference Objecten_US

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