A Survey on Detection of Diabetic Retinopathy

Visual perception is very important for human life. Although several medical conditions can cause retinal disease, the most common cause is diabetes. Diabetic Retinopathy (DR) can be identified using retinal fundus images. Detection and classification of deformation in Diabetic retinopathy is a challenging task since it is symptomless. Several algorithms were analyzed for the identification of abnormality. The analysis of different models in detecting the abnormalities from the image is done which includes various preprocessing techniques to standardize the image and post-processing techniques are applied for morphological adjustments, segmentation algorithms for segmenting the Lesion of Interest(LOI ) namely white lesions and red lesions, further feature extraction methods extracts the features like Micro Aneurysms, Hemorrhages, Exudates and Cotton Wool Spots and so on finally, classification methods were utilized which concludes the presence or absence of DR symptoms along with the severity based on the count of the features extracted in the given retinal image. This survey study aims to develop a novel algorithm to identify and detect types of above mentioned diseases and find out the severity of those diseases also examine with 100% accuracy.