State-of-Art Review on Automated Glaucoma Detection Using Machine Learning Techniques

Abstract

Glaucoma is one of the biggest factors of blindness as it leads to illness of neuro-degeneration. Early detection can help in controlling its progression although it cannot be cured. A systematic analysis was conducted on the basis of publications acquired from Scopus, IEEE Xplore, Science Direct and PubMed databases. Focus was on the papers comprising computer aided diagnosis techniques of glaucoma. As evident from literature, there is a possibility of developing an automated system for detection of glaucoma by employing existing techniques of deep learning, machine learning etc. This paper provides a systematic overview of techniques which aids glaucoma detection based on Fundus images using computer-aided methods. Results obtained from comparative analysis of various techniques help in identifying challenges presented from image processing to machine learning and suitable technique of detection as per the types of glaucoma.