Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.

Glaucoma, the second largest cause of irreversible blindness worldwide, causes significant damage to the optic nerve. Early diagnosis of glaucoma is crucial since without it, there will be continuous deterioration of vision. Manual detection of glaucoma based on fundus images is a tedious and potentially inaccurate process, making it important to develop computer-aided glaucoma detection systems. This paper introduces a hybrid approach to glaucoma detection that involves the use of Local Binary Pattern (LBP) and Pivot Distribution Count (PDC) feature extraction methods for the analysis of retinal fundus images. LBP is a technique that involves the extraction of texture-based features, whereas PDC is a feature extraction algorithm that involves the extraction of white-pixel intensity and fractal dimension from retinal fundus images. In this study, features were extracted and classified using different machine learning algorithms such as SVM, Decision Trees (DT), Random Forest (RF), KNN, Adaboost, Gradient Boosting, XGboost, Light Gradient Boosting Machine, and CatBoost. Moreover, grid search cross validation, randomized search cross validation, genetic algorithm, and Bayesian optimization were also applied to optimize the performance of LightGBM and Catboost classifiers. The proposed Hybrid LBP-PDC method provided maximum classification accuracy of 97.55% using the CatBoost and LightGBM classifiers. Moreover, we have developed a robust and efficient methodology for the automatic glaucoma screening using the retinal fundus image analysis.