A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images

TL;DR

This paper presents a new computer-aided diagnosis system for glaucoma that combines image enhancement, graph-based feature extraction, and wavelet neural networks, outperforming existing methods in accuracy.

Contribution

Introduces a novel glaucoma classification method using wavelet neural networks with graph-based features from enhanced retinal images, improving diagnostic robustness.

Findings

WNN outperforms multilayer perceptron classifiers.

Enhanced retinal images improve classification accuracy.

Proposed approach surpasses existing glaucoma diagnosis methods.

Abstract

In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis. Our objective is to introduce a CAD system with a fresh approach. Retinal image quality improvement is attempted in two phases. The retinal image preprocessing phase improves the brightness and contrast of the image through quantile based histogram modification. It is followed by the image enhancement phase, which involves multi scale morphological operations using image specific dynamic structuring elements for the retinal structure enrichment. Graph based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced retinal dataset. WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets. The results show our approach is superior to the existing approaches.