Abstract
Image processing and machine learning techniques are used for automatic detection of abnormalities in eye. The proposed methodology requires a clear photograph of eye (not necessarily a fundoscopic image) from which the chromatic and spatial property of the sclera and iris is extracted. These features are used in the diagnosis of various diseases considered. The changes in the colour of iris is a symptom for corneal infections and cataract, the spatial distribution of different colours distinguishes diseases like subconjunctival haemorrhage and conjunctivitis, and the spatial arrangement of iris and sclera is an indicator of palsy. We used various classifiers of which adaboost classifier which was found to give a substantially high accuracy i.e., about 95% accuracy when compared to others (k-NN and naive-Bayes). To enumerate the accuracy of the method proposed, we used 150 samples in which 23% were used for testing and 77% were used for training.
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Acknowledgments
We would like to thank Dr. Punith Kumar, MBBS, MS (OPHTHOL), Varun Eye Clinic, Bangalore for his valuable suggestions and timely feedbacks given to us in building this model.
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Prashasthi, M., Shravya, K.S., Deepak, A., Mulimani, M., Shashidhar, K.G. (2017). Image Processing Approach to Diagnose Eye Diseases. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_24
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DOI: https://doi.org/10.1007/978-3-319-54430-4_24
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