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Diabetic Retinopathy Classification Using C4.5

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Knowledge Management and Acquisition for Intelligent Systems (PKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11016))

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Abstract

Early detection of diabetic retinopathy (DR) can prevent blindness and improve the quality of life. Practical detection requires a cost-effective screening over a large population. The presence of Microaneurysms (MAs) in a retinal image is the earliest sign of DR. This paper presents an efficient method to automatically detect MAs in a retinal image. The method is based on an advanced wavelet transform and the C4.5 algorithm (a categorization algorithm to distinguish DR and non-DR cases). It uses both the green and red channel data in RGB retinal images for detection of small sized MAs and obtains image feature parameters from the input image. A system was developed to implement the proposed method that displayed a sensitivity of 0.92 and a precision of 0.82.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, 2308, Australia

    Mira Park & Peter Summons

Authors
  1. Mira Park
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  2. Peter Summons
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Corresponding author

Correspondence to Mira Park .

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Editors and Affiliations

  1. University of Tsukuba , Tokyo, Japan

    Kenichi Yoshida

  2. Shih Chien University, Taipei City, Taiwan

    Maria Lee

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Park, M., Summons, P. (2018). Diabetic Retinopathy Classification Using C4.5. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-97289-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97288-6

  • Online ISBN: 978-3-319-97289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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