TY - GEN
T1 - Image processing approach to diagnose eye diseases
AU - Prashasthi, M.
AU - Shravya, K. S.
AU - Deepak, Ankit
AU - Mulimani, Manjunath
AU - Shashidhar, Koolagudi G.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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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U2 - 10.1007/978-3-319-54430-4_24
DO - 10.1007/978-3-319-54430-4_24
M3 - Conference contribution
AN - SCOPUS:85018493843
SN - 9783319544298
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 254
BT - Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
A2 - Tojo, Satoshi
A2 - Nguyen, Le Minh
A2 - Nguyen, Ngoc Thanh
A2 - Trawinski, Bogdan
PB - Springer Verlag
T2 - 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Y2 - 3 April 2017 through 5 April 2017
ER -