Eyesight is one of the most vital senses. In the year 2021, about 2.2 billion people will have vision impairment. Amongst them, 93 million were due to cataracts. In this paper, we have merged different image processing techniques and deep learning networks to diagnose and differentiate between various types of cataracts. The conventional Convolution Neural Network (CNN), in conjunction with support vector machines (SVM), classifies nuclear, cortical spoking, and capsular cataract eyes. The proposed method was successful in accurately classifying the two classes with an accuracy of 85.71%, while for a three-class classification, 83.33% was the maximum accuracy achieved.

Keratoconus is a progressive eye disease prevalent worldwide. Keratoconus is caused by the change in curvature of the eyeball. An unevenly shaped lens causes blurry and inaccurate vision in the patient, which eventually may cause blindness. The existing keratoconus detection techniques use a less accurate keratoscope and bulky, expensive topography imaging (OCT) device, which causes inconvenience in diagnosing this disease in a remote and scarce setup. In this paper, I propose a novel smartphone-based keratoconus diagnosis technique that uses a smartphone camera to acquire a 2D image of the cornea with Placido disc reflection, and process different image processing techniques including entropy-based edge detection, multiple circles detections, finally calculating 3D topography of the eye in an app. The existing inconvenient methods of keratoconus detection can be replaced by our proposed method, which is accurate, quick, reliable, and simple for usage by clinicians and patients in remote settings.