Enhanced Retinal Image Analysis Using Machine Learning Based Image Processing

Retinal image examination is an effective approach to the diagnosis and examination of ocular pathological conditions including diabetic retinopathy, glaucoma and age related macular degeneration. Conventional approaches to analyzing retinal images are quite slow and, in many cases, inaccurate. This paper tries to develop a machine learning model to improve the five different features of the retinal image using some complex image analysis. Our work also shows that CNNs and other machine learning methods lead to enhanced segmentation, feature selection, and categorization of images of the retina. The proposed methodology is then tested on a large dataset to document better accuracy compared to traditional processes. The study shows that machine learning may help identify the onset of retinal diseases, and would improve clinical management of the conditions.