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- W4313444405 abstract "Automatic vessel segmentation is in corking demand as it requires expertise and experience of ophthalmologist. Presently CNN is more popular in the field of computer vision, due to its higher accuracy, but it requires large data. On the other side medical imaging has limited high resolution samples in the dataset. The proposed model utilizes patch-based input, to avoid the issue of down sampling and overfitting. As retinal vasculature varies in shape and width throughout its length, the proposed model adopted the different size kernel for dilated convolution to extract different size features without using pooling layer. This results into deduction in computational cost, nearly five times than the conventional neural network model as well it projects a notable improvement in the segmentation process." @default.
- W4313444405 created "2023-01-06" @default.
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- W4313444405 date "2023-01-01" @default.
- W4313444405 modified "2023-09-27" @default.
- W4313444405 title "Semantic Segmentation of Retinal Vasculature Using Light Patch-Based Dilated CNN" @default.
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- W4313444405 doi "https://doi.org/10.1007/978-981-19-2358-6_26" @default.
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