Grading Severity of Cataract Using Fuzzy Reasoning

Severity assessment of eye related diseases plays a foremost role in saving the sight of the human being by treating in the early stages. One of the major eye problems occurs in the old age people is the development of the white thickest layer called cataract on the lens. This part of the paper presents the development of the model which is able to detect the cataract and also assess the severity level. The development of the model is done in two stages. In the first stage cataract images are preprocessed, segmented and 3 dominant features were extracted. In the second stage identification of the presence of the cataract and estimation of the degree of damage is done by using Fuzzy inference system. In this work whiteness spread and thickness considered as the new feature which can help in assessing the severity of the cataract. Due to the non-uniformity of the spreading of the white deposit on the lens initiate to use Fussy Inference system (FIS) for assessment. The developed model shows good accuracy in classify the different grades in the range of 85% - 92%. The parameters specificity, sensitivity is also consider in this work for accuracy measures.