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Diabetic Retinopathy Severity Prediction Using Deep Learning Techniques

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  • Victer Paul

    (Indian Institute of Information Technology, Kottayam, India)

  • Bivek Benoy Paul

    (Indian Institute of Information Technology, Kottayam, India)

  • R. Raju

    (Sri Manakula Vinayagar Engineering College, India)

Abstract

Diabetic retinopathy is one of the leading causes of visual loss and with timely diagnosis, this condition can be prevented. This research proposes a transfer learning-based model that is trained using retinal fundus images of patients whose severity is graded by trained ophthalmologists into five different classifications. The research uses transfer learning based on a pre-trained model that is ResNet 50, thus it is possible to train the model with the limited amount of labeled training data. The model has been trained and its accuracy has been analyzed using different metrics namely accuracy score, loss graph and confusion matrix. Such deep learning models need to be transparent for approval by the regulatory authorities for clinical use. The clinical practitioner also needs to have information about the working of the classification method to make sure that he/she understands the decision making process of the model.

Suggested Citation

  • Victer Paul & Bivek Benoy Paul & R. Raju, 2023. "Diabetic Retinopathy Severity Prediction Using Deep Learning Techniques," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 19(1), pages 1-19, January.
    • Handle: RePEc:igg:jiit00:v:19:y:2023:i:1:p:1-19
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.329929
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    References listed on IDEAS

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    1. Yu Wang & Xi Liu & Chongchong Yu & Yanxia Sun, 2021. "Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion," Complexity, Hindawi, vol. 2021, pages 1-10, April.
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