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Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

Azim Uslucuk1,
Hakan Öcal2
1Bartın University
2Bartın University
Published:December 31, 2023
DOI: 10.56038/oprd.v3i1.309
Vol. 3, No. 1 · pp. 146–157

Abstract

The pancreas is one of the vital organs in the human body. It has an essential role in the digestive system and endocrine system. Diseases such as cancer, diabetes, hormonal problems, pancreatitis, and digestive problems occur in pancreatic disorders. In detecting pancreatic disorders, first blood and urine tests are requested. If further examination is needed, CT (Computed Tomography), MR (Magnetic Resonance), and EUS (Endoscopic Ultrasonography) imaging methods are used. Pancreas segmentation is generally the process of defining and drawing the lines of the pancreas from medical images such as CT and MRI. The size and shape of the pancreas varies from person to person. Manual segmentation of the pancreas is time-consuming and varies between physicians. Recently, deep learning-based segmentation methods that achieve high-performance results in organ segmentation have become trendy. In this study, Unet and Vnet architectures were comparatively analyzed on the NIH-CT-82 dataset. As a result of the ablation studies, a validation sensitivity of 0.9978 and a validation loss of 0.041 were obtained in the Unet architecture. In the training with the Vnet architecture, 0.9975 validation sensitivity and 0.046 validation loss values were obtained, respectively.

Keywords
Artificial IntelligenceDeep LearningArtificial Neural NetworksPancreasSegmentation

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Cite This Article
Uslucuk, A., Öcal, H. (2023). Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation. *Orclever Proceedings of Research and Development*, 3(1), 146-157. https://doi.org/10.56038/oprd.v3i1.309

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume3
Issue1
Pages146–157
PublishedDecember 31, 2023
eISSN2980-020X