A Novel Approach for Glaucoma Classification by Wavelet Neural Networks Using Statistical and Graph-Based Features of Qualitatively Improved Images

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, computer-aided diagnosis (CAD) substantially aids in robust diagnosis.Our proposal has a significant contribution to make in all major CAD phases.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 structural enhancement phase, which involves multi-scale morphological operations using image-specific dynamic structuring elements (SE).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 images.WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function.The performance of the WNN classifier is compared with that of multilayer perceptron neural networks (MLP) and the state-of-the art approaches to prove its efficacy.