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Retinal fundus image classification for diabetic retinopathy using SVM predictions.

Minal Hardas1, Sumit Mathur2, Anand Bhaskar2

  • 1Electronics & Communication Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India. minal.sudarshan@spsu.ac.in.

Physical and Engineering Sciences in Medicine
|June 9, 2022
PubMed
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This study introduces an automated method for detecting Diabetic Retinopathy (DR), a leading cause of blindness. The technique uses machine learning to classify DR abnormalities, aiming for earlier disease detection and diagnosis.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Science

Background:

  • Diabetic Retinopathy (DR) is a primary cause of vision loss across all age groups.
  • DR arises from retinal vascular issues like inadequate blood supply, exudation, and hemorrhage.
  • Current DR diagnosis and treatment present challenges for both clinicians and patients.

Purpose of the Study:

  • To develop a comprehensive and automated DR screening technique for early disease detection.
  • To implement a 16-class classification system using Support Vector Machine (SVM) for DR abnormality prediction.
  • To reduce the computational cost associated with DR diagnosis.

Main Methods:

  • Utilized a combination of Gaussian Mixture Model (GMM), K-means, Maximum a Posteriori (MAP), Principal Component Analysis (PCA), and Grey Level Co-occurrence Matrix (GLCM).
Keywords:
Diabetic retinopathyFundus imageGrey level co-occurrence matrixSupport vector machine

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  • Employed Support Vector Machine (SVM) for the 16-class classification of DR abnormalities.
  • Evaluated the proposed method on the DIARETDB1 dataset.

    • Main Results:

    • Achieved an accuracy of 77.3% in classifying Diabetic Retinopathy on the DIARETDB1 dataset.
    • The integrated approach demonstrated potential for individual or combined abnormality prediction.
    • The method is designed for low computational cost, facilitating practical application.

    Conclusions:

    • The proposed automated system offers a promising approach for early Diabetic Retinopathy screening and diagnosis.
    • The combination of image processing techniques and SVM classification aids in identifying DR-related abnormalities.
    • The low computational demand suggests feasibility for integration into clinical workflows for improved DR management.