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A computational modeling for the detection of diabetic retinopathy severity

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done...

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Detalles Bibliográficos
Autores principales: Mishra, Pavan Kumar, Sinha, Abhijit, Teja, Kaveti Ravi, Bhojwani, Nitin, Sahu, Sagar, Kumar, Awanish
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209363/
https://www.ncbi.nlm.nih.gov/pubmed/25352722
http://dx.doi.org/10.6026/97320630010556
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author Mishra, Pavan Kumar
Sinha, Abhijit
Teja, Kaveti Ravi
Bhojwani, Nitin
Sahu, Sagar
Kumar, Awanish
author_facet Mishra, Pavan Kumar
Sinha, Abhijit
Teja, Kaveti Ravi
Bhojwani, Nitin
Sahu, Sagar
Kumar, Awanish
author_sort Mishra, Pavan Kumar
collection PubMed
description Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.
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spelling pubmed-42093632014-10-28 A computational modeling for the detection of diabetic retinopathy severity Mishra, Pavan Kumar Sinha, Abhijit Teja, Kaveti Ravi Bhojwani, Nitin Sahu, Sagar Kumar, Awanish Bioinformation Hypothesis Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR. Biomedical Informatics 2014-09-30 /pmc/articles/PMC4209363/ /pubmed/25352722 http://dx.doi.org/10.6026/97320630010556 Text en © 2014 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Hypothesis
Mishra, Pavan Kumar
Sinha, Abhijit
Teja, Kaveti Ravi
Bhojwani, Nitin
Sahu, Sagar
Kumar, Awanish
A computational modeling for the detection of diabetic retinopathy severity
title A computational modeling for the detection of diabetic retinopathy severity
title_full A computational modeling for the detection of diabetic retinopathy severity
title_fullStr A computational modeling for the detection of diabetic retinopathy severity
title_full_unstemmed A computational modeling for the detection of diabetic retinopathy severity
title_short A computational modeling for the detection of diabetic retinopathy severity
title_sort computational modeling for the detection of diabetic retinopathy severity
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209363/
https://www.ncbi.nlm.nih.gov/pubmed/25352722
http://dx.doi.org/10.6026/97320630010556
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