Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy
 
Yazarlar (4)
Zeki Kucukkara
Selçuk Üniversitesi, Türkiye
Doç. Dr. İlker Ali Özkan Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Doç. Dr. Onur Ceylan Selçuk Üniversitesi, Türkiye
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Veterinary Parasitology (Q1)
Dergi ISSN 0304-4017 Dergi Bilgileri (2025)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 01-2025
Kabul Tarihi Yayınlanma Tarihi 01-02-2025
Cilt / Sayı / Sayfa 334 / 1 / 110400–0 DOI 10.1016/j.vetpar.2025.110400
Makale Linki https://doi.org/10.1016/j.vetpar.2025.110400
UAK Araştırma Alanları
Yapay Zeka
Özet
Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by intestinal damage, hemorrhagic diarrhea, lethargy, and high mortality rates in the absence of effective control measures. The rapid spread of these parasites through ingestion of food and drinking water can seriously endanger animal health and productivity, leading to significant economic losses in the chicken industry. Chicken Eimeria species are difficult to identify by conventional microscopy due to similarities in oocyst morphologies. In addition, species identification, which is significant in epidemiological studies, is a time-consuming process involving the sporulation stage and various measurements, requiring labor and expertise. Therefore, the objective of this study was to develop an automated system to classify digital micrographic images of sporulated Eimeria …
Anahtar Kelimeler
Chicken coccidiosis | Deep transfer learning | Digital micrograph analysis | Poultry | Xception model
Science Direct
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Web of Science 3
Scopus 2
Google Scholar 6
Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy

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