1. Discrimination of diabetic retinopathy from optical coherence tomography angiography images using machine learning methods

    Discrimination of diabetic retinopathy from optical coherence tomography angiography images using machine learning methods

    Purpose: The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from 3×3 mm scans with the assistance of different machine learning models. Methods: The OCTA angiography dataset of superficial vascular plexus (SVP), deep vascular plexus (DVP), and retinal vascular network (RVN) were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. Four machine learning models, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), support vector machine ...

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