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Implementation of Deep Learning Neural Network for Retinal Images

Implementation of Deep Learning Neural Network for Retinal Images

R. Murugan
Copyright: © 2020 |Pages: 19
ISBN13: 9781522599029|ISBN10: 1522599029|ISBN13 Softcover: 9781522599036|EISBN13: 9781522599043
DOI: 10.4018/978-1-5225-9902-9.ch005
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MLA

Murugan, R. "Implementation of Deep Learning Neural Network for Retinal Images." Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, IGI Global, 2020, pp. 77-95. https://doi.org/10.4018/978-1-5225-9902-9.ch005

APA

Murugan, R. (2020). Implementation of Deep Learning Neural Network for Retinal Images. In S. Velayutham (Ed.), Handbook of Research on Applications and Implementations of Machine Learning Techniques (pp. 77-95). IGI Global. https://doi.org/10.4018/978-1-5225-9902-9.ch005

Chicago

Murugan, R. "Implementation of Deep Learning Neural Network for Retinal Images." In Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, 77-95. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9902-9.ch005

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Abstract

The retinal parts segmentation has been recognized as a key component in both ophthalmological and cardiovascular sickness analysis. The parts of retinal pictures, vessels, optic disc, and macula segmentations, will add to the indicative outcome. In any case, the manual segmentation of retinal parts is tedious and dreary work, and it additionally requires proficient aptitudes. This chapter proposes a supervised method to segment blood vessel utilizing deep learning methods. All the more explicitly, the proposed part has connected the completely convolutional network, which is normally used to perform semantic segmentation undertaking with exchange learning. The convolutional neural system has turned out to be an amazing asset for a few computer vision assignments. As of late, restorative picture investigation bunches over the world are rapidly entering this field and applying convolutional neural systems and other deep learning philosophies to a wide assortment of uses, and uncommon outcomes are rising constantly.

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