Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm
AuthorHussain, MA; Bhuiyan, A; Luu, CD; Smith, RT; Guymer, RH; Ishikawa, H; Schuman, JS; Ramamohanarao, K
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sLuu, Chi; Luu, Chi; Guymer, Robyn; Kotagiri, Ramamohanarao; Hussain, Md Akter
AffiliationComputing and Information Systems
Ophthalmology (Eye & Ear Hospital)
Centre for Eye Research Australia (CERA)
Document TypeJournal Article
CitationsHussain, M. A., Bhuiyan, A., Luu, C. D., Smith, R. T., Guymer, R. H., Ishikawa, H., Schuman, J. S. & Ramamohanarao, K. (2018). Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm. PLOS ONE, 13 (6), https://doi.org/10.1371/journal.pone.0198281.
Access StatusOpen Access
ARC Grant codeARC/DP110102621
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.
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References