Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study

Abstract

This paper addresses the use of computer-aided diagnosis (CAD) system for the cataract classification based on ultrasound technique. Ultrasound A-scan signals were acquired in 220 porcine lenses. B-mode and Nakagami images were constructed. Ninety-seven parameters were extracted from acoustical, spectral and image textural analyses and were subjected to feature selection by Principal Component Analysis (PCA). Bayes, K Nearest-Neighbors (KNN), Fisher Linear Discriminant (FLD) and Support Vector Machine (SVM) classifiers were tested. The classification of healthy and cataractous lenses shows a good performance for the four classifiers (F-measure ≥92.68%) with SVM showing the highest performance (90.62%) for initial versus severe cataract classification.