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...

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Vydané v:	PloS one Ročník 13; číslo 6; s. e0198281
Hlavní autori:	Hussain, Md Akter, Bhuiyan, Alauddin, D. Luu, Chi, Theodore Smith, R., H. Guymer, Robyn, Ishikawa, Hiroshi, S. Schuman, Joel, Ramamohanarao, Kotagiri
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 04.06.2018 Public Library of Science (PLoS)
Predmet:	Age Algorithms Area Under Curve Artificial intelligence Automation Biology and Life Sciences Care and treatment Classification Computer and Information Sciences Diabetes Diabetes mellitus Diabetic retinopathy Diabetic Retinopathy - diagnostic imaging Diabetic Retinopathy - pathology Diagnosis Edema Eye Feature extraction Female Humans Image classification Information systems International conferences Macular degeneration Macular Degeneration - diagnostic imaging Macular Degeneration - pathology Male Medical imaging Medicine and Health Sciences Methods Optical Coherence Tomography Optics Phenotypes Physical Sciences Research and Analysis Methods Retina Retina - diagnostic imaging Retina - pathology Retinal Diseases - classification Retinal Diseases - diagnostic imaging Retinal Diseases - pathology Retinal Drusen - diagnostic imaging Retinal Drusen - pathology ROC Curve Tomography Tomography, Optical Coherence - methods New York Melbourne Victoria Australia Australia United States > US
ISSN:	1932-6203, 1932-6203
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0198281