Design of an Automatic System to Determine the Degree of Progression of Diabetic Retinopathy

Hernando González; Carlos Arizmendi; Jessica Aza

doi:10.1007/978-3-030-55187-2_4

Design of an Automatic System to Determine the Degree of Progression of Diabetic Retinopathy

Hernando González, Carlos Arizmendi, Jessica Aza

Research output: Events › Scientific events › peer-review

Abstract

This paper proposes an analysis and detection of diabetic retinopathy by using artificial vision technics, such as filtering, transforms, edge detection and segmentation on color fundus images to recognize and categorize microaneurysm, hemorrhages and exudates. The algorithms were validated with the DIARETDB database. Of the processed images are determined the descriptors for the design of two classifiers, the first based on vector support machines and the second with neural networks.

Original language	English
Pages	35-44
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-55187-2_4
State	Published - 2021
Event	Intelligent Systems Conference, IntelliSys 2020 - London, United Kingdom Duration: 3 Sep 2020 → 4 Sep 2020

Conference

Conference	Intelligent Systems Conference, IntelliSys 2020
Country/Territory	United Kingdom
City	London
Period	3/09/20 → 4/09/20

Keywords

Diabetic retionopathy
Exudates
Hemorrhages
Microaneurysm
Neural network
Support Vector Machine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-55187-2_4

Cite this

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title = "Design of an Automatic System to Determine the Degree of Progression of Diabetic Retinopathy",

abstract = "This paper proposes an analysis and detection of diabetic retinopathy by using artificial vision technics, such as filtering, transforms, edge detection and segmentation on color fundus images to recognize and categorize microaneurysm, hemorrhages and exudates. The algorithms were validated with the DIARETDB database. Of the processed images are determined the descriptors for the design of two classifiers, the first based on vector support machines and the second with neural networks.",

keywords = "Diabetic retionopathy, Exudates, Hemorrhages, Microaneurysm, Neural network, Support Vector Machine",

author = "Hernando Gonz{\'a}lez and Carlos Arizmendi and Jessica Aza",

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AU - Arizmendi, Carlos

AU - Aza, Jessica

PY - 2021

Y1 - 2021

N2 - This paper proposes an analysis and detection of diabetic retinopathy by using artificial vision technics, such as filtering, transforms, edge detection and segmentation on color fundus images to recognize and categorize microaneurysm, hemorrhages and exudates. The algorithms were validated with the DIARETDB database. Of the processed images are determined the descriptors for the design of two classifiers, the first based on vector support machines and the second with neural networks.

AB - This paper proposes an analysis and detection of diabetic retinopathy by using artificial vision technics, such as filtering, transforms, edge detection and segmentation on color fundus images to recognize and categorize microaneurysm, hemorrhages and exudates. The algorithms were validated with the DIARETDB database. Of the processed images are determined the descriptors for the design of two classifiers, the first based on vector support machines and the second with neural networks.

KW - Diabetic retionopathy

KW - Exudates

KW - Hemorrhages

KW - Microaneurysm

KW - Neural network

KW - Support Vector Machine

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Y2 - 3 September 2020 through 4 September 2020

ER -

Design of an Automatic System to Determine the Degree of Progression of Diabetic Retinopathy

Abstract

Conference

Keywords

UN SDGs

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