Glaucoma is an eye decease that can be recognized as the second most common cause of blindness. Glaucoma is an irreversible decease, so that it is necessary to prevent from glaucoma before the complete loss of sight. Manual screening of glaucoma among larger amount of count is complex due to the availability of experienced manpower in Ophthalmology is less. The research focuses on the analysis each and every features of retinal image in glaucoma and builds an optimistic automatic glaucoma screening system with reduced complexity. Presently, there are so many treatments are available to prevent vision loss due to glaucoma, but it should be detected in the begging stage. Thus, the objective is to develop an automated identification method of Glaucoma from retinal images. The steps involved in this work are Disc segmentation, texture feature extraction in different colour models and classification of images in glaucomatous or not. The obtained results having 94% accuracy.
Valarmathi, R., Rajasundari, K., & Loganayaki*, G. (2020). Classification of Glaucoma with OD Segmentation and Texture Feature Extraction using Random Committee. International Journal of Innovative Technology and Exploring Engineering, 9(7), 462–466. https://doi.org/10.35940/ijitee.g5242.059720