Feature Selection Using Fuzzy-based Firefly Algorithm for Glistenings Detection on Intraocular Lenses

Glistenings are liquid-filled microvacuoles in intraocular lenses (IOLs) appear when the IOL is in an aquatic environment that affect the quality of vision. In our glistenings Detection method, the candidate glistenings are automatically detected by mathematic morphology methodology. Machine learning approaches, feature selection and classification are used in this paper. The 68 features are extracted and used as training data for fine segmented using the classifiers. The detected glistenings are validated by object-based with ophthalmologist’s hand-drawn ground-truth. Our proposed method, Feature Selection using Fuzzy-based Firefly Algorithm (FS-FFA) applied the concept of fuzzy entropy to calculating the membership of features data for in order to select good sets of the relevant features that maximize the classification performance in glistenings Detection. The proposed FS-FFA is compared with feature selection methods the standard firefly algorithm (FS-FA) and without feature selection using basic classifier k-nearest neighbor. The results have shown that the Matthews correlation coefficient (MCC) and the diagnostic odds ratio (DOR) value increase after feature selection using firefly algorithm and fuzzy entropy. Small size of features set also decreased that classification time in testing phase.