<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-davvxH eCVTlP"><div class="sc-jCNfQM ikRdXB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><p><strong>Dataset Overview:</strong></p>

<ul>

<li>80 of the images in the RGB retina <a aria-label="dataset (opens in a new tab)" target="_blank" href="https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images">dataset</a> (<a aria-label="https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images (opens in a new tab)" target="_blank" href="https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images">https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images</a>) obtained from the previous study were manually segmented by experts.</li>

<li>A Samsung tablet and tablet pen were used during the segmentation process.</li>

<li>The size of the segmented images is 1536×1152 pixels.</li>

<li>Manually segmented retinal images were used to train the deep learning models and the performance of the models was tested with 1200 RGB images. </li>

<li>The images used for the test were taken from the RGB images available at: (<a aria-label="https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images) (opens in a new tab)" target="_blank" href="https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images)">https://www.kaggle.com/datasets/animalbiometry/cattle-retinal-fundus-images)</a>.</li>

</ul>

<p><strong>Example manually segmented retinal images used in model training:</strong><br>

<img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20186453%2Fa40243ba5a47ca5d4a5e54f2326d789e%2Frrrrr.png?generation=1739963998283037&amp;alt=media"></p>

<p><strong>Sample RGB retinal images used for testing:</strong><br>

<img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20186453%2F200542ea6fbc1b07b0b1d5984440e03f%2FResim1.png?generation=1714030338216733&amp;alt=media"></p>

<p><strong>Citation</strong><br>

If you use the Cattle Retinal Fundus Images, please cite the dataset paper:<br>

Ahmet Saygılı, Pınar Cihan, Celal Şahin Ermutlu, Uğur Aydın, Özgür Aksoy,<br>

CattNIS: Novel identification system of cattle with retinal images based on feature matching method,<br>

Computers and Electronics in Agriculture, 221 (2024): 108963, <a rel="noreferrer nofollow" aria-label="https://doi.org/10.1016/j.compag.2024.108963 (opens in a new tab)" target="_blank" href="https://doi.org/10.1016/j.compag.2024.108963">https://doi.org/10.1016/j.compag.2024.108963</a>.</p>

<p><strong>Funding:</strong></p>

<ul>

<li>This project was supported by the Turkish Scientific and Technical Research Council-TÜBİTAK (Project Number: 121E349).</li>

</ul>

<p><strong>Ethics Committee Approval:</strong></p>

<ul>

<li>The study was approved from the Kafkas University Animal Experiments Local Ethics Committee (Protocol number: KAU-HADYEK/2021-132).</li>

<li>The study was approved from the Kafkas University Animal Experiments Local Ethics Committee (Protocol number: KAÜ-HADYEK/2024-123).</li>

</ul></div></div></div>