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Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Image and Video Processing 2023-03-03 v1 Computer Vision and Pattern Recognition
Authors: Maryam Viqar , Violeta Madjarova , Amit Kumar Yadav , Desislava Pashkuleva , Alexander S. Machikhin
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Abstract

Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

Keywords

optical coherence tomography image segmentation deep learning u-net atrous convolution residual blocks varicose vein

Cite

@article{arxiv.2303.01054,
  title  = {Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein},
  author = {Maryam Viqar and Violeta Madjarova and Amit Kumar Yadav and Desislava Pashkuleva and Alexander S. Machikhin},
  journal= {arXiv preprint arXiv:2303.01054},
  year   = {2023}
}

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