Machine Learning based Prediction of Retinopathy Diseases using Segmented Images

Diabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats...

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Autor: Saroj, Sushil Kumar
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/162542
Acceso en línea:http://hdl.handle.net/10366/162542
Access Level:acceso abierto
Palabra clave:Segmented images
Machine learning
Feature extraction
Classification
id ES_5017fd2e2cda2b50bab29df8cbe898da
oai_identifier_str oai:gredos.usal.es:10366/162542
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
title Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
spellingShingle Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
Saroj, Sushil Kumar
Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
title_short Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
title_full Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
title_fullStr Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
title_full_unstemmed Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
title_sort Machine Learning based Prediction of Retinopathy Diseases using Segmented Images
dc.creator.none.fl_str_mv Saroj, Sushil Kumar
Saroj, Sushil Kumar
Saroj, Sushil Kumar
author Saroj, Sushil Kumar
author_facet Saroj, Sushil Kumar
author_role author
dc.subject.none.fl_str_mv Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
topic Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
Segmented images
Machine learning
Feature extraction
Classification
description Diabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats. Blood vessels of a retina contain information about these diseases. Therefore, features extraction from retinal vessels and classification of these diseases are essential. There are existing different approaches today to classify these diseases, but they have used RGB retinal images due to which their performances are relatively low. In this paper, we have proposed an approach based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma and multi class diseases. We have conducted exhaustive experiments on large number of images of DRIVE, STARE and HRF datasets. The accuracy of the proposed approach is 90.90%, 95.00%, and 92.90% for diabetic retinopathy, glaucoma, and multi class diseases, respectively which is found better than most of the approaches of this area.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/162542
url http://hdl.handle.net/10366/162542
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407860306214912
spelling Machine Learning based Prediction of Retinopathy Diseases using Segmented ImagesSaroj, Sushil KumarSaroj, Sushil KumarSaroj, Sushil KumarSegmented imagesMachine learningFeature extractionClassificationSegmented imagesMachine learningFeature extractionClassificationSegmented imagesMachine learningFeature extractionClassificationDiabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats. Blood vessels of a retina contain information about these diseases. Therefore, features extraction from retinal vessels and classification of these diseases are essential. There are existing different approaches today to classify these diseases, but they have used RGB retinal images due to which their performances are relatively low. In this paper, we have proposed an approach based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma and multi class diseases. We have conducted exhaustive experiments on large number of images of DRIVE, STARE and HRF datasets. The accuracy of the proposed approach is 90.90%, 95.00%, and 92.90% for diabetic retinopathy, glaucoma, and multi class diseases, respectively which is found better than most of the approaches of this area.Diabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats. Blood vessels of a retina contain information about these diseases. Therefore, features extraction from retinal vessels and classification of these diseases are essential. There are existing different approaches today to classify these diseases, but they have used RGB retinal images due to which their performances are relatively low. In this paper, we have proposed an approach based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma and multi class diseases. We have conducted exhaustive experiments on large number of images of DRIVE, STARE and HRF datasets. The accuracy of the proposed approach is 90.90%, 95.00%, and 92.90% for diabetic retinopathy, glaucoma, and multi class diseases, respectively which is found better than most of the approaches of this area.Diabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats. Blood vessels of a retina contain information about these diseases. Therefore, features extraction from retinal vessels and classification of these diseases are essential. There are existing different approaches today to classify these diseases, but they have used RGB retinal images due to which their performances are relatively low. In this paper, we have proposed an approach based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma and multi class diseases. We have conducted exhaustive experiments on large number of images of DRIVE, STARE and HRF datasets. The accuracy of the proposed approach is 90.90%, 95.00%, and 92.90% for diabetic retinopathy, glaucoma, and multi class diseases, respectively which is found better than most of the approaches of this area.Ediciones Universidad de Salamanca (España)202520252024info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10366/162542reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1625422026-06-07T06:28:51Z
score 15,81155

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