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dc.contributor.authorPrashasthi, M.-
dc.contributor.authorShravya, K.S.-
dc.contributor.authorDeepak, A.-
dc.contributor.authorMulimani, M.-
dc.contributor.authorKoolagudi, S.G.-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, Vol.10192 LNAI, , pp.245-254en_US
dc.description.abstractImage 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. � Springer International Publishing AG 2017.en_US
dc.titleImage processing approach to diagnose eye diseasesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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