Evaluation of Convolutional Neural Network Model for Classifying Red and Healthy Eye
Sherry Verma1, Latika Singh2, Monica Chaudhry3
1Sherry Verma*, School of Engineering and Technology, Ansal University, Gurgaon, India.
2Latika Duhan, School of Engineering and Technology, Ansal University, Gurgaon, India.
3Monica Chaudhry, Sushant School of Health Science, Ansal University, Gurgaon, India.
Manuscript received on September 14, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 5067-5071 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25681081219/2019©BEIESP | DOI: 10.35940/ijitee.L2568.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human eye is covered with mucous like thin membrane on the outer surface as well as inner surface of the eyelids. The thin membrane covering the outer surface is called as bulbar conjunctiva. Conjunctiva hyperemia refers to the redness of the conjunctiva. There are several reasons for this condition, some of which are related to pathologies like trauma, allergy, injury, prolonged use of lenses or glaucoma. Hence, it is one of the most vital parameter to diagnose these pathologies. For populous developing countries like India, ratio of ophthalmologist to citizen is highly skewed which negatively affects the public health. This gives rise to need for developing technology based solutions for initial screening of eye problems like conjunctiva hyperemia. This paper presents a framework for classifying normal versus red eyes using deep learning technique of convolution neural network (CNN). The model has shown promising results with 94% accuracy.
Keywords: Hyperemia, Convolutional Neural Network, Bulbar Redness, Healthy Eyes.
Scope of the Article: Healthcare Informatics