Extraction Zoning Feature to Diabetic Retinopathic Detection Models

  • Authors

    • Erwin Sirait
    • Muhammad Zarlis
    • Syahril Efendi
    2018-06-20
    https://doi.org/10.14419/ijet.v7i3.2.18757
  • Bayes Methid, Detection, Diabetic Retinopathic

  • Abstract

    The health sector is one area that has been applying various computer technologies. To diagnose a patient's illness was already done with computers. One is to diagnose diabetic Retinopathic disease that can happen to anyone. Diabetic Retinopathy, which is one of the complications caused by diabetes. Symptoms shown from this disease is mikroneurisma, hemorrhages, excudets and neovascularos. The detection of the disease is done by looking at the information on the retinal image and can then be classified according to severity. This research aims to develop a method that can be used utuk classify Diabetic Retinopathy. The process of classification is based    fiture-fiture the retinal image obtained by the extraction process using extraction methods Zoning. The process is then performed to classify the Bayes Method and the results obtained Diabetic Retinopahty classification. The results of this study yield maximum   accuracy 65%.

     

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  • How to Cite

    Sirait, E., Zarlis, M., & Efendi, S. (2018). Extraction Zoning Feature to Diabetic Retinopathic Detection Models. International Journal of Engineering & Technology, 7(3.2), 786-788. https://doi.org/10.14419/ijet.v7i3.2.18757

    Received date: 2018-09-02

    Accepted date: 2018-09-02

    Published date: 2018-06-20