Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform

Abstract

Glaucoma is an eye disease in which fluid within the eye rises and puts pressure on optic nerves. This fluid pressure slowly damages the optic nerves, and if it is left untreated, it may lead to permanent vision loss. So the detection of glaucoma is necessary for on-time treatment. This paper presents a method, namely two dimensional Fourier-Bessel series expansion based empirical wavelet transform (2D-FBSE-EWT), which uses the Fourier-Bessel series expansion (FBSE) spectrum of order zero and order one for boundaries detection. 2D-FBSE-EWT method is also studied on multi-frequency scale during boundaries detection in FBSE spectrum. In multi-frequency scale based 2D-FBSE-EWT analysis, three frequency scales full, half, and quarter are used. These methods are used for the decomposition of fundus images into sub-images. For glaucoma detection from sub-images, two methods are used: (1) proposed method-1, which is a conventional machine learning (ML) based method and (2) proposed method-2, which is an ensemble ResNet-50 based method. The ensemble is done using operations like maxima, minima, averages, and fusion. Proposed method-1 has provided best result with order one 2D-FBSE-EWT at full scale. In Proposed method-2, order one 2D-FBSE-EWT at full scale with fusion ensemble method provides better accuracy as compared to other ensemble methods. Our proposed methods have outperformed all the compared methods used for glaucoma detection. © 2020 Elsevier Ltd