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    AI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models
    (Elsevier Sci Ltd, 2024) Cihan, Pinar; Saygili, Ahmet; Ermutlu, Celal Sallin; Aydin, Ugur; Aksoy, Ozgur
    Cardiovascular 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.
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    CattNIS: Novel identification system of cattle with retinal images based on feature matching method
    (Elsevier Sci Ltd, 2024) Saygili, Ahmet; Cihan, Pinar; Ermutlu, Celal Sahin; Aydin, Ugur; Aksoy, Ozgur
    Small and large farm animals are frequently identified using techniques like microchipping or tagging. However, these techniques may have a detrimental impact on animal welfare and may result in theft and fraud. Modern and sophisticated identification procedures using biometrics, including retina, face, and iris recognition, are available and have less of a detrimental effect on animal welfare. Because biometric markers are unchangeable and unaffected, it is feasible to stop situations of theft and fraud. This study uses retina images to identify animals based on each animal ' s different and specific retinal patterns. To the best of our knowledge, there isn ' t a publicly accessible dataset for images of animal retinas. The three main goals of this study are to: 1) produce a novel dataset of Cattle retinal images and make it publicly available; 2) create a successful image processing -based system for retinal identification and recognition; and 3) create a graphical user interface (GUI) that enables querying the database to ascertain whether a specific Cattle retina is present. To accomplish these goals, a dataset comprising 2430 images from the left and right eyes of 300 cattle was produced. After the dataset was created, the animal retinal images were segmented using image processing methods such as scaling, color transformations, image sharpening, contrast enhancements, noise filtering, and histogram equalization. By using the segmented images with the SURF, FAST, BRISK, and HARRIS techniques, distinctive retinal features were found. During the identification phase, the animal whose retinal characteristic matched the sought animal ' s retina the closest was recognized. Within the developed CattNIS system, the SURF approach has the highest accuracy rate of 92.25 percent. The results of this study show that the identification of cattle using retinal vascular patterns is highly successful. The availability of the collected cattle retinal images from this study is expected to significantly contribute to the advancement of future research in this field, both in terms of quantity and quality.