Performance Analysis of Machine Learning Algorithms for Strabismus Detection – A Systematic Review

Performance Analysis of Machine Learning Algorithms for Strabismus Detection – A Systematic Review
Author(s):
S. NANCY LIMA CHRISTY, DR. S. NITHYAKALYANI, G. NAGARAJAN
Keywords:
Strabismus (Squint Eye or Crossed Eye), Machine Learning, Accuracy, Sensitivity, Specificity, Convolution Neural Network - CNN, Support vector machine -SVM.
Abstract
In recent days statistical reports focus those 8 to 10 conditions in kids whenever analysed early can forestall youth visual impairment which incorporates youth Strabismus (Squint Eye), Amblyopia (Lazy Eye) and so on, metropolitan regions have 1 ophthalmologist for 10,000 individuals yet in country zones, it is 1 for every 2,50,000 individuals. This paper provides a systematic study based mainly on strabismus detection surveys using Machine Learning techniques. Among all Machine Learning Algorithms Convolution Neural Network - CNN produces more accuracy of 95.83%. than Support vector machine -SVM.
Article Details
Unique Paper ID: 155376 Publication Volume & Issue: Volume 8, Issue 10 Page(s): 65 - 72
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