Identification of Chicken Eimeria Species from Microscopic Images by Using MLP Deep Learning Algorithm

Basit Öğe Kaydı
dc.contributor.authorBuyukyilmaz, Mucahit
dc.contributor.authorCibikdiken, Ali Osman
dc.contributor.authorAbdalla, Mohamed A. E.
dc.contributor.authorSeker, Huseyin
dc.date.accessioned2024-02-23T14:26:27Z
dc.date.available2024-02-23T14:26:27Z
dc.date.issued2017
dc.departmentNEÜen_US
dc.descriptionInternational Conference on Video and Image Processing (ICVIP) -- DEC 27-29, 2017 -- Singapore, SINGAPOREen_US
dc.description.abstractEimeria has more than one species of every single genus of animals that causes diseases that may spread at fast speed and therefore adversely affects animal productivities and results in animal death. It is therefore essential to detect the disease and prevent its spread at the earliest stage. There have been some attempts to address this problem through the analysis of microscopic images. However, due to the complexity, diversity, and similarity of the types of the species, there need more sophisticated methods to be adapted for the intelligent and automated analysis of their microscopic images by using machine-learning methods. To tackle this problem, a deep-learning-based architecture has been proposed and successfully adapted in this study where Chicken fecal microscopy images have been analyzed to identify nine types of these species. The methodology developed includes two main parts, namely (i) pre-processing steps include the techniques that convert image into gray level, extract cell walls, remove background, rotate cell to vertically aligned position and move to their center and (ii) MLP-based deep learning technique to learn features and classify the images, for which Keras model was utilized. Based on the outcome of a 5-fold cross validation that was repeated for 100 times, the approach taken has yielded an average accuracy of 83.75%+/- 0.60, which is comparable to the existing methods.en_US
dc.description.sponsorshipNecmettin Erbakan University, BAP Coordination Officeen_US
dc.description.sponsorshipThis study is financially supported by the Necmettin Erbakan University, BAP Coordination Office.en_US
dc.identifier.doi10.1145/3177404.3177445
dc.identifier.endpage88en_US
dc.identifier.isbn978-1-4503-5383-0
dc.identifier.scopus2-s2.0-85045845590en_US
dc.identifier.startpage84en_US
dc.identifier.urihttps://doi.org/10.1145/3177404.3177445
dc.identifier.urihttps://hdl.handle.net/20.500.12452/14196
dc.identifier.wosWOS:000463778500016en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAssoc Computing Machineryen_US
dc.relation.ispartofProceedings Of 2017 International Conference On Video And Image Processing (Icvip 2017)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectImage Processingen_US
dc.titleIdentification of Chicken Eimeria Species from Microscopic Images by Using MLP Deep Learning Algorithmen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu