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Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm
In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retina...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986153/ https://www.ncbi.nlm.nih.gov/pubmed/29864167 http://dx.doi.org/10.1371/journal.pone.0198281 |
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author | Hussain, Md Akter Bhuiyan, Alauddin D. Luu, Chi Theodore Smith, R. H. Guymer, Robyn Ishikawa, Hiroshi S. Schuman, Joel Ramamohanarao, Kotagiri |
author_facet | Hussain, Md Akter Bhuiyan, Alauddin D. Luu, Chi Theodore Smith, R. H. Guymer, Robyn Ishikawa, Hiroshi S. Schuman, Joel Ramamohanarao, Kotagiri |
author_sort | Hussain, Md Akter |
collection | PubMed |
description | In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy. |
format | Online Article Text |
id | pubmed-5986153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59861532018-06-16 Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm Hussain, Md Akter Bhuiyan, Alauddin D. Luu, Chi Theodore Smith, R. H. Guymer, Robyn Ishikawa, Hiroshi S. Schuman, Joel Ramamohanarao, Kotagiri PLoS One Research Article In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy. Public Library of Science 2018-06-04 /pmc/articles/PMC5986153/ /pubmed/29864167 http://dx.doi.org/10.1371/journal.pone.0198281 Text en © 2018 Hussain et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hussain, Md Akter Bhuiyan, Alauddin D. Luu, Chi Theodore Smith, R. H. Guymer, Robyn Ishikawa, Hiroshi S. Schuman, Joel Ramamohanarao, Kotagiri Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title_full | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title_fullStr | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title_full_unstemmed | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title_short | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
title_sort | classification of healthy and diseased retina using sd-oct imaging and random forest algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986153/ https://www.ncbi.nlm.nih.gov/pubmed/29864167 http://dx.doi.org/10.1371/journal.pone.0198281 |
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