{'fraction_non_alphanumeric': 0.02529960053262317, 'fraction_numerical': 0.0, 'mean_word_length': 5.714285714285714, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Progress on combining OCT-A with deep learning for diabetic retinopathy diagnosis

We present novel approaches of implementing state-of-the-art deep learning techniques for the processing of optical coherence tomography angiography (OCT-A) images for the classification of diabetic retinopathy (DR) severity. The effects of feature-engineering on a deep neural network’s classification performance is compared against unprocessed OCT-A images. We investigate the effects of lower axial resolution (simulated by using a narrower spectral bandwidth) on the classification of DR severity, and the recovery of lost features using a generative adversarial network. We also explore the relationship between DR severity classification and lateral resolution.