Glaucoma Detection Using Fundus Images of The Eye

Carrillo, Juan; Bautista, Lola; Villamizar, Jorge; Rueda, J. A.; Sanchez, Mary Alejandra; Rueda, Daniela

doi:10.1109/stsiva.2019.8730250

Cited by 53 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Keratoconus is a condition where the cornea of the eye starts getting thinner in progress. The cornea is nothing but the outer layer of the eye in the front [1]. Patients having this keratoconus eye disease will have a coneshaped cornea.…”

Section: Introductionmentioning

confidence: 99%

“…The thickest part of the cornea is its middle layer, which is mainly made up of the protein known as collagen and water. [1] [3] This protein called collagen makes the cornea solid and elastic, which helps keep the regular round shape.…”

Section: Introductionmentioning

confidence: 99%

“…In India there is a significant increase in the number of cases of the keratoconus, and several research centres have been paying attention to this disease in recent years. In this situation, there is an immediate need for tools that simplify both diagnosis and treatment [1]. The algorithm developed can decide whether the eye is normal eye or keratoconus eye with stages.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

Impana¹,

Bhoomika²,

S³

et al. 2021

JUSST

View full text Add to dashboard Cite

Keratoconus eye disease is not an inflammatory corneal disease that is caused by progress in thinning of the cornea, scarring, and deformation in the shape of the cornea. In India, there is a significant increase in the number of cases of keratoconus, and several research centers have been paying attention to this disease in recent years. In this situation, there is an immediate need for tools that simplify both diagnosis and treatment[1]. The algorithm developed can decide whether the eye is a normal eye or keratoconus eye with stages. The K-net model analyzes the pentagram images of the eye using a convolutional neural network(CNN) a deep learning model and pre-trained ResNet-50 and InceptionV3 pre-trained models and does the comparative analysis of the accuracies of these models. The results show that the Keratoconus Detection algorithm leads to a good job, with a 93.75 percent accuracy on the data test collection. Keratoconus Detection model is a program that can help ophthalmologists test their patients faster, therefore reducing diagnostic errors and facilitating treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

Impana¹,

Bhoomika²,

S³

et al. 2021

JUSST

View full text Add to dashboard Cite

show abstract

“…Localizing Optic disk and Optic cup is a challenge task due to intensity variations within the Optic disk and its vicinity. Juan Carrillo, Lola Bautista et al [1] gives computational tool for automatic glaucoma detection from fundus image of the eye. The presence of the cup in the disc is a strong indicator of glaucoma, a method to detect glaucoma was presented by properly detecting the location of the cup.…”

Section: Introductionmentioning

confidence: 99%

A Review on Glaucoma Disease Detection by using Fundus Images of Eye

Rabade¹

2020

IJRASET

View full text Add to dashboard Cite

Glaucoma is the Second leading causes of blindness across the world, it is an eye disease that affects the retina and optic nerve of eye which carries signals to the eye. This disease can leads to permanent blindness if not treated at earlier stage. The increased intraocular pressure which is main cause of glaucoma damages the optic nerve which sends images to the brain, the diameter of the optic cup within optic disc region is increased due to this increased retinal pressure.The increased cup to disc ratio (CDR) results in the loss of optic nerve fibers that are connected to the retina by disc area. CDR is the important structural features for differentiating between healthy and a glaucomatous eye. The objective of this paper is to present review of methods for automated detection of Glaucoma from fundus images of eye that assist in the progressive development of computer aided systems.

show abstract

“…A imagem de fundo de olhoé uma das técnicas não invasivas mais utilizadas pelos oftalmologistas [Chen et al 2015], os especialistas medem a proporção CDR (Cup to Disc Ratio) entre o tamanho da região do DO e do CDO. Na prática, o que os médicos fazem para determinar se o paciente tem glaucoma, ou não,é estimar visualmente o CDR observando a imagem de fundo de olho, sendo esta uma tarefa subjetiva, que pode levar de 1 a 3 minutos se feita por um médico especialista experiente, mas que pode ser uma tarefá ardua em casos de campanhas de triagem, onde no final os especialistas tem centenas de imagens para analisar [Carrillo et al 2019]. Na maioria dos casos o glaucomaé assintomático no início e vai, progressivamente, causando uma lesão irreversível no nervo.…”

Section: Introductionunclassified

Comparação das Técnicas de Extração de Características HOG e LBP para Deteção de Glaucoma em Retinografias

Jesus¹,

Komati²,

Simões³

2020

Anais Do Encontro Nacional De Computação Dos Institutos Federais (EnCompIF 2020)

View full text Add to dashboard Cite

A Organização Mundial da Saúde (OMS) considera o glaucoma como a doença ocular crônica que é a segunda maior causa de cegueira no mundo. Existem diversas técnicas de aprendizado de máquina que podem auxiliar na detecção do glaucoma. Neste artigo, avaliamos três combinações: dez diferentes canais de espaço de cores, dois extratores de características (HOG e LBP) com três diferentes parâmetros em cada e cinco diferentes algoritmos de aprendizado de máquina. Os melhores resultados na base de dados Drishti foram alcançados em 4 combinações diferentes, indicando que tanto o HOG quanto o LBP discriminam texturas, e que o classificador SVM, com kernel RBF ou linear, atingem acurácia de 88,23%.

show abstract

Glaucoma Detection Using Fundus Images of The Eye

Cited by 53 publications

References 9 publications

Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

A Review on Glaucoma Disease Detection by using Fundus Images of Eye

Comparação das Técnicas de Extração de Características HOG e LBP para Deteção de Glaucoma em Retinografias

Contact Info

Product

Resources

About