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ARDC-UNet retinal vessel segmentation with adaptive residual deformable convolutional based U-Net

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Abstract

To extract maximum features ResAttNet (RAN) network structure is chosen as an alternative to the convolutional layer and it enhances image feature extraction. Additionally, a Deformable Convolution (DC) network was included to provide a feature extraction module, improving the model's capacity to simulate vessel deformation. Apart from the two additional networks because of inadequate quality in retinal data, before model building pre-processing is done. The data is processed by CLAHE, normalization, grayscale transformation, and gamma transformation. Second, the fundamental network structure model U-net is constructed, and the ResAttNet (RAN) structure and DC network are combined to form the ARDC-UNet network. Experimental data, both quantitative and qualitative, demonstrate the efficiency and accuracy with which our ARDC-UNet can segment retinal vessels.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of CS&SE, Andhra University, Visakhapatnam, India

    N. V. Naik & P. V. G. D. Prasad Reddy

  2. Department of CSE, Gandhi Institute of Technology and Management (GITAM) University, Visakhapatnam, India

    Hyma J

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  1. N. V. Naik
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Correspondence to N. V. Naik.

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Naik, N.V., J, H. & Reddy, P.V.G.D.P. ARDC-UNet retinal vessel segmentation with adaptive residual deformable convolutional based U-Net. Multimed Tools Appl 83, 78747–78768 (2024). https://doi.org/10.1007/s11042-024-18603-3

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