{'fraction_non_alphanumeric': 0.014830508474576272, 'fraction_numerical': 0.005296610169491525, 'mean_word_length': 5.176470588235294, '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}}
An Offbeat Technique for Diabetic Retinopathy Detection using Computer Vision

Diabetic Retinopathy, also known as diabetic eye disease, is a complication that arises due to a disease called Diabetes Mellitus. It damages blood vessels in the eyes due to high blood sugar levels causing progressive damage to the retina which can thereby cause blindness. The main objective is to develop an Automated System that can distinguish diseased eyes from healthy ones based on High Resolution Fundus Image of the Retina. A variety of Image Processing techniques are applied which includes Greyscale Conversion, Thresholding and Binarization. The processed images are then fed into a Feedforward Convolutional Neural Network-SVM Hybrid to classify them on the basis of the patient being diabetic or not. The results, so obtained are a 100% Validation Accuracy and Recall of 1.0, eventually breaking the benchmark model performance of the existing works.