Quantitative Analysis of Retinal Vasculature for Eye Disease Identification Using Deep Learning

Non-invasive Medical imaging and diagnosis is one of the prime areas of medical research these days. There exist many challenges in the field of eye care, especially treatment, preventive quality, and visual rehabilitation services among others. In this paper, we attempt to study and draw correlations between conditions such as Diabetic Retinopathy, Hypertensive Retinopathy, Myopia etc. and their effects on various retinal parameters and the vasculature for a speedier diagnosis of eye diseases. Various parameters of the eye such as Cup-to-Disc Ratio and morphological feature measurements such as vessel density, average width, fractal dimension and various measures of tortuosity are studied for each disease to find out how these conditions manifest in the colour fundus photographs of the retina of people suffering from different diseases. Early detection and diagnosis of several eye diseases would prevent patients from complete visual impairment.