Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial as it may help to decelerate the progress. The traditional instrument methods are manual, time-consuming and less accurate. Hence, the automated diagnosis of glaucoma is needed for detection of glaucoma in the early stage with high accuracy. The flexible analytic wavelet transform (FAWT) based novel method has been proposed for the classification of glaucoma stages. In the proposed method, FAWT has been used to decompose the preprocessed images into various sub-band images. Then, ReliefF and sequential box-counting (SBC) algorithms are applied to extract the various entropies and fractal dimension (FD) based features, respectively. Further, the extracted feature values are ranked using Fisher's linear discriminant analysis (LDA) dimensionally reduction. Finally, the higher rank features have been used for the classification of glaucoma stages using least squares-support vector machine (LS-SVM) classifier. The proposed method has been evaluated on publicly available large and diverse glaucoma database. The classification accuracy of the proposed method is 93.40% using tenfold cross-validation. The proposed method has demonstrated better performance for glaucoma classification as compared to the existing methods. The proposed method is ready to help the ophthalmologist in their daily screening for glaucoma detection.