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@article{155376,
        author = {S. NANCY LIMA CHRISTY and DR. S. NITHYAKALYANI and G. NAGARAJAN},
        title = {Performance Analysis of Machine Learning Algorithms for Strabismus Detection – A Systematic Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {10},
        pages = {65-72},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155376},
        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.},
        keywords = {Strabismus (Squint Eye or Crossed Eye), Machine Learning, Accuracy, Sensitivity, Specificity, Convolution Neural Network - CNN, Support vector machine -SVM.},
        month = {},
        }