Classification of Histopathological Images Using Convolutional Neural Network
| dc.authorid | Bilgin, Gokhan/0000-0002-5532-477X | |
| dc.authorwosid | Bilgin, Gokhan/W-2666-2018 | |
| dc.contributor.author | Hatipoglu, Nuh | |
| dc.contributor.author | Bilgin, Gokhan | |
| dc.date.accessioned | 2024-06-12T11:03:20Z | |
| dc.date.available | 2024-06-12T11:03:20Z | |
| dc.date.issued | 2014 | |
| dc.department | Trakya Üniversitesi | en_US |
| dc.description | International Conference on Image Processing Theory, Tools and Applications (IPTA) -- OCT 14-17, 2014 -- Paris, FRANCE | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.sponsorship | IEEE France Sect,Univ Evry Val Essonne,Informat Biol Integrat & Complex Syst Lab,Inst Technologie UnivEvry Val Essonne,GENOPLOLE,Mutual General Natl Educ,Cooperat Bank Staff Natl Educ Res & Culture,European Assoc Signal Proc,IEEE | en_US |
| dc.identifier.endpage | 300 | en_US |
| dc.identifier.isbn | 978-1-4799-6463-5 | |
| dc.identifier.issn | 2154-512X | |
| dc.identifier.startpage | 295 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.14551/21606 | |
| dc.identifier.wos | WOS:000380617200052 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2014 4th International Conference On Image Processing Theory, Tools And Applications (Ipta) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Histopathological Images | en_US |
| dc.subject | Convolutional Neural Networks | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Image Processing | en_US |
| dc.title | Classification of Histopathological Images Using Convolutional Neural Network | en_US |
| dc.type | Conference Object | en_US |
