NDLI: Identification of Cataract and Post-cataract Surgery Optical Images Using Artificial Intelligence Techniques

Content Provider	Springer Nature Link
Author	Acharya, Rajendra Udyavara Yu, Wenwei Zhu, Kuanyi Nayak, Jagadish Lim, Teik Cheng Chan, Joey Yiptong
Copyright Year	2009
Abstract	Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.
Starting Page	619
Ending Page	628
Page Count	10
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	34
Issue Number	4
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2009-05-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cataract Normal Neural network Classifier Histogram ANOVA test Sensitivity Specificity Training Accuracy Neuron Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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