Isfahan University of Medical Sciences

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Automatic Classification of Retinal Three-Dimensional Optical Coherence Tomography Images Using Principal Component Analysis Network With Composite Kernels Publisher Pubmed

Summary: Scientists report an AI method accurately classifies 3D eye scans, improving diagnosis of retinal diseases. #EyeHealth #ArtificialIntelligence

Fang L1 ; Wang C1 ; Li S1 ; Yan J1 ; Chen X2 ; Rabbani H3
Authors

Source: Journal of Biomedical Optics Published:2017


Abstract

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
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