Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

İbrahim Atlı; O. Serdar Gedık

doi:10.1016/j.jestch.2020.07.008

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Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

İbrahim Atlı O. Serdar Gedık

Year: 2020 Journal: Engineering Science and Technology an International Journal Vol: 24 (2)Pages: 271-283 Publisher: Elsevier BV

DOI: 10.1016/j.jestch.2020.07.008

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Keywords:

Computer science Segmentation Deep learning Artificial intelligence Robustness (evolution) Computer vision Convolutional neural network Rendering (computer graphics) Medical imaging Machine learning Pattern recognition (psychology)

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Topics

Retinal Imaging and Analysis

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Retinal and Optic Conditions

Health Sciences → Medicine → Ophthalmology

Retinal Diseases and Treatments

Health Sciences → Medicine → Ophthalmology

Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

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Citation History

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