A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine

Abstract

The role of diabetes mellitus in deteriorating the visual health of diabetic subjects has been affirmed precisely. The study of morphological features near the macular region is the most common method of investigating the impairment rate. The general mode of diagnosis carried out by manual inspection of fundus imaging, is less effective and slow. The goal of this study is to provide a novel approach to classify optical coherence tomography images effectively and efficiently. discrete wavelet transform and fast fourier transform are utilized to extract features, and a kernel-based support vector machine is used as classifier. To improve image contrast, histogram equalization is performed. Segmentation of the enhanced images is performed using k-means clustering. The hybrid feature extraction technique comprising the discrete wavelet transform and fast fourier transform renders novelty to the study. In terms of classification accuracy, the system's efficiency is compared to that of earlier available techniques. The suggested approach attained an overall accuracy of 96.46 % over publicly available datasets. The classifier accuracy of the system is found to be better than the performance of the discrete wavelet transform with self organizing maps and support vector machines with a linear kernel.