AI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models

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dc.contributor.authorCihan, Pinar
dc.contributor.authorSaygili, Ahmet
dc.contributor.authorErmutlu, Celal Sallin
dc.contributor.authorAydin, Ugur
dc.contributor.authorAksoy, Ozgur
dc.date.accessioned2024-10-29T17:58:23Z
dc.date.available2024-10-29T17:58:23Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractCardiovascular diseases (CVD) in animals can severely impact the heart and circulatory systems, like those in humans. Early diagnosis and treatment are crucial for improving animal welfare and lifespan. Traditional diagnostic methods face challenges such as insufficient anamnesis information, high costs of biochemical and hematological tests, and increasing data complexity. This study aims to address these issues by developing AIbased diagnostic systems for fast and accurate CVD diagnosis in cattle using retinal images. A total of 1118 retinal images from 100 cattle were collected, with 52 diagnosed with CVD and 48 as non-CVD. The dataset is publicly available on Kaggle. We evaluated three machine learning methods (Extreme Learning Machine, KNearest Neighbors, and Support Vector Machine) and four deep learning models (DenseNet201, ResNet101, SqueezeNet, and InceptionV3) for their diagnostic capabilities. ResNet101 emerged as the most effective model, achieving an accuracy of 96.1 f 3.15 %, sensitivity of 97.3 f 2.96 %, specificity of 94.9 f 4.07 %, and an F1score of 96.4 f 0.03. This study demonstrates that AI-based systems, particularly deep learning models, can significantly improve the accuracy of CVD diagnosis in animals, marking a critical advancement in veterinary healthcare.
dc.description.sponsorshipTurkish Scientific and Technical Research Council-TUEBITAK [121E349]
dc.description.sponsorshipThis work was supported by the Turkish Scientific and Technical Research Council-TUEBITAK (Project Number: 121E349) .
dc.identifier.doi10.1016/j.compag.2024.109391
dc.identifier.issn0168-1699
dc.identifier.issn1872-7107
dc.identifier.scopus2-s2.0-85202342534
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compag.2024.109391
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14283
dc.identifier.volume226
dc.identifier.wosWOS:001307843900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofComputers and Electronics in Agriculture
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCardiovascular diseases
dc.subjectRetina
dc.subjectImage processing
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectComputer-based diagnosis
dc.titleAI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models
dc.typeArticle

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