Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy
Veterinary Parasitology · Şubat 2025
YÖKSİS Kayıtları
Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy
Veterinary Parasitology · 2025 SCI-Expanded
Doç. Dr. ONUR CEYLAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Expression profile and polymorphisms of actin genes in protoscoleces of Echinococcus granulosus from sheep in central Turkey Doktora tez çalışmasından üretilmiştir
2009 ISSN: 0304-4017 SCI 1 atıf
Prof. Dr. HİLAL ARIKOĞLU →
A new immunoreactive recombinant protein designated as rBoSA2 from Babesia ovis Its molecular characterization subcellular localization and antibody recognition by infected sheep
2015 ISSN: 03044017 SCI-Expanded
Prof. Dr. FERDA SEVİNÇ →
Instability of ovine babesiosis in an endemic area in Turkey
2012 ISSN: 03044017 SCI-Expanded
Prof. Dr. FERDA SEVİNÇ →
Babesia ovis infections Detailed clinical and laboratory observations in the pre and post treatment periods of 97 field cases
2013 ISSN: 03044017 SCI-Expanded 5 atıf
Prof. Dr. NERMİN IŞIK USLU →
Determination of immunoreactive proteins of Babesia ovis
2013 ISSN: 03044017 SCI-Expanded
Prof. Dr. NERMİN IŞIK USLU →
Therapeutic and prophylactic efficacy of imidocarb dipropionate on experimental Babesia ovis infection of lambs
2007 ISSN: 03044017 SSCI 19 atıf
Prof. Dr. FERDA SEVİNÇ →
A comparative study on the prevalence of Theileria equi and Babesia caballi infections in horse sub populations in Turkey
2008 ISSN: 03044017 SSCI
Prof. Dr. MEHMET MADEN →
A new immunoreactive recombinant protein designated as rBoSA2 from Babesia ovis Its molecular characterization subcellular localization and antibody recognition by infected sheep
2015 ISSN: 0304-4017 SCI-Expanded
Doç. Dr. ONUR CEYLAN →
Haemoparasitic agents associated with ovine babesiosis: A possible negative interaction between Babesia ovis and Theileria ovis
2018 ISSN: 0304-4017 SCI-Expanded
Prof. Dr. MUTLU SEVİNÇ →
Endemic instability of ovine babesiosis in Turkey: A country-wide sero-epidemiological study
2020 ISSN: 0304-4017 SCI-Expanded
Prof. Dr. FERDA SEVİNÇ →
Untargeted metabolomics to discriminate liver and lung hydatid cysts: Importance of metabolites involved in the immune response
2024 ISSN: 0304-4017 SCI Q2
Prof. Dr. SALİH MAÇİN →
Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy
2025 ISSN: 0304-4017 SCI-Expanded Q2
Doç. Dr. ONUR CEYLAN →
Makale Bilgileri
Dergi
Veterinary Parasitology
ISSN03044017
Yayın TarihiŞubat 2025
Cilt / Sayfa334
Scopus ID2-s2.0-85215839282
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 oocysts belonging to seven pathogenic species obtained from domestic chickens using deep transfer learning (DTL) models. This study is the first to utilize feature extraction and fine-tuning methods for classification using DTL models. In this study, 17 pre-trained DTL models were utilized for the classification process. The Xception model achieved the highest classification performance with an accuracy rate of 96.4 %, outperforming all the other models. These results highlight the efficacy of the Xception model and show that DTL models have significant potential in classifying Eimeria species. The DTL models applied in this study, which use both feature extraction and fine-tuning methods to enable species classification of sporulated oocysts of primary chicken Eimeria species, may reduce the workload of researchers in the future and can be incorporated into diagnostic tools and adapted for other practical uses in parasitology and other scientific fields.
Yazarlar (4)
1
Zeki Kucukkara
ORCID: 0000-0002-5204-0819
2
Ilker Ali Ozkan
3
Şakir Taşdemir
4
Onur Ceylan
Anahtar Kelimeler
Chicken coccidiosis
Deep transfer learning
Digital micrograph analysis
Poultry
Xception model
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Veterinary Parasitology
Q1
SJR Skoru0,636
H-Index156
YayıncıElsevier B.V.
ÜlkeNetherlands
Veterinary (miscellaneous) (Q1)
Medicine (miscellaneous) (Q2)
Parasitology (Q2)
Metrikler
1
Atıf
4
Yazar
5
Anahtar Kelime