Evaluation of machine learning techniques for glaucoma recognition and prediction

Glaucoma is a quiet vision thief. Early detection of glaucoma is almost difficult, and there is currently no treatment for glaucoma in its latter stages. This research looked at a variety of automated glaucoma detection methods. A thorough review of the literature was performed on preprocessing, feature extraction, feature selection, Machine Learning methods, and data sets utilized for testing and training. Automated glaucoma prediction is critical, but sadly, only a small amount of work has been done in this area, and only a minimal level of accuracy has been reached. However, automated glaucoma detection has progressed to the point that most machine learning methods can correctly identify 85 percent of glaucoma patients. Glaucoma can be predicted successfully using Optical Coherence Tomography.