Optimal Feature Selection and Extraction for Eye Disease Diagnosis

Abstract

Ophthalmologists utilize retinal fundus images of humans for the detection, diagnosis, and prediction of many eye diseases. Automatic scrutiny of fundus images are foremost apprehension for ophthalmologists and investigators. The manual recognition of blood vessels is most deceptive because the blood vessels in a fundus image are multifaceted and with low contrast. Unearthing of blood vessels proffers information on pathological transformation and can smooth the progress of rating diseases severity or mechanically diagnosing the diseases. The manual recognition method turns out to be annoying. Consequently, the automatic recognition of blood vessels is also more significant. For extracting the vessel in fundus images unswerving and habitual methods are obligatory. The proposed methodology is designed to effectively diagnose the eye disease by performing feature extraction succeeded by feature selection and to improve the performance factors such as feature extraction ratio, feature selection time, sensitivity, and specificity when compared to the state-of-art methods.