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https://www.selleckchem.com/products/bal-0028.html 73%, which was higher than that of the existing methods with significant difference ( < 0.01). Experimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance. Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR. Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR. To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomography angiography (OCTA) images and their corresponding co-registered structural images. A total of 463 volumes from 380 eyes were acquired using the 3 × 3-mm OCTA protocol on the Zeiss Plex Elite system. Enface images of the superficial and deep capillary plexus were exported from both the optical coherence tomography and OCTA data. Component neural networks were constructed using single data-types and fine-tuned using VGG19, ResNet50, and DenseNet architectures pretrained on ImageNet weights. These networks were then ensembled using majority soft voting and stacking techniques. Results were compared with a classifier using manually engineered features. Class activation maps (CAMs) were created using the original CAM algorithm and Grad-CAM. The networks trained with the VGG19 architecture outperformed the networks trained on deeper architectures. Ensemble networks constructed using the four fine-tuned VGG19 architectures achieved accuracies of 0.92 and 0.90 for the majority soft voting and stacking methods respectively. Both ensemble methods outperformed the highest single data-type network and the network trained on hand-crafted features. Grad-CAM was shown to more accurately highlight areas of disease. Ensemble learning

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