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In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and the results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy). © 2017 IEEE.
| Yazar |
Aydemir, Zübeyr Erkaymaz, Okan Ferah, Meryem Akpolat |
| Yayın Türü | Proceedings |
| Tek Biçim Adres | https://hdl.handle.net/20.500.12628/4606 |
| Konu Başlıkları |
artificial neural network
cell counting image processing immunohistochemistry |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü Bildiri Koleksiyonu (Bilgisayar Mühendisliği Bölümü) Fakülteler Tıp Fakültesi Temel Tıp Bilimleri Bildiri Koleksiyonu (Temel Tıp Bilimleri) Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD Scopus İndeksli Yayınlar Koleksiyonu |
| Dergi Adı | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
| Sayfalar | - |
| Yayın Yılı | 2017 |
| Eser Adı [dc.title] | Cell counting and recognition of immunohistochemically dyed seminiferous tubules with feed-forward neural network |
| Yazar [dc.contributor.author] | Aydemir, Zübeyr |
| Yazar [dc.contributor.author] | Erkaymaz, Okan |
| Yazar [dc.contributor.author] | Ferah, Meryem Akpolat |
| Yayın Yılı [dc.date.issued] | 2017 |
| Yayıncı [dc.publisher] | Institute of Electrical and Electronics Engineers Inc. |
| Yayın Türü [dc.type] | proceedings |
| Açıklama [dc.description] | 25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703 |
| Özet [dc.description.abstract] | In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and the results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy). © 2017 IEEE. |
| Kayıt Giriş Tarihi [dc.date.accessioned] | 2019-12-23 |
| Açık Erişim Tarihi [dc.date.available] | 2019-12-23 |
| Yayın Dili [dc.language.iso] | tur |
| Konu Başlıkları [dc.subject] | artificial neural network |
| Konu Başlıkları [dc.subject] | cell counting |
| Konu Başlıkları [dc.subject] | image processing |
| Konu Başlıkları [dc.subject] | immunohistochemistry |
| Haklar [dc.rights] | info:eu-repo/semantics/closedAccess |
| Alternatif Başlık [dc.title.alternative] | İmmünohistokimyasal boyanmış seminifer tübül hücrelerinin ileri beslemeli yapay sinir ağı yardımıyla sayılması ve tanınması |
| Dergi Adı [dc.relation.journal] | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
| Tek Biçim Adres [dc.identifier.uri] | https://dx.doi.org/10.1109/SIU.2017.7960511 |
| Tek Biçim Adres [dc.identifier.uri] | https://hdl.handle.net/20.500.12628/4606 |