Abstract

Background: Cataract is a disease that occurs when alterations in the eye lens result in blurred vision. These alterations are caused due to protein accumulation in the lens of an eye. It can lead to the decrease in vision and some loss of eyesight.

Methods: The detection of cataract is normally done using slit-lamp exam, retinal examination, refraction and visual acuity test. However, it is possible to detect cataract with image processing and machine learning techniques while making its clinical analysis quite easier.

Discussion: This paper discusses different techniques for automatic categorization and classification of cataract. These techniques present considerable possibilities to lessen the load of qualified ophthalmologists.

Conclusion: Cataract sufferers in underdeveloped areas can get benefit by automated cataract classification to recognize their cataract conditions at the right time.

Keywords: Classification, cloudiness, fundus image, classifier, sensitivity, specificity.

Graphical Abstract