Open AccessAn Automated Detection and Classification of Cataract and Glaucoma Using RCNN
Author(s) -
E T Alida
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36172
Subject(s) - glaucoma , economic shortage , blindness , grading (engineering) , medicine , optometry , computer science , ophthalmology , artificial intelligence , engineering , linguistics , philosophy , civil engineering , government (linguistics)
one of the human’s deterioration is visual impairment. Cataract and Glaucoma are the most prevailing cause blindness in the world. Early detection and treatment is the best way to prevent the blindness. Currently grading is done by human graders, it is found to be time taking and grading is usually subjective. Computer aided analysis can help human graders. An automated cataract and glaucoma detection and classification approach is proposed in this paper, to grade more objectively. Region based convolution neural network (RCNN) is used to classification process. The percentage of accuracy of classification obtained for cataract and glaucoma is 98.9% and 97.8% respectively. The method is especially suitable for cataract and glaucoma screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.