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Early diagnosis of diabetic retinopathy using unsupervised learning

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Abstract

The main cause of eye disorders and visual loss in diabetics is diabetic retinopathy (DR). Patients with diabetes frequently have DR as a result of damage to retinal blood vessels. Therefore, the detachment of the retinal blood vessels is essential to the diagnosis of DR. If the diagnosis is made in the early stages, we can avoid vision loss or blindness issues. A 50% reduction in the probability of vision loss would result from an early diagnosis and initial investigation. Basic preprocessing, segmentation of blood arteries utilizing primary curvatures (maximum) and the ISODATA algorithm with the aid of image processing techniques, and labeling of related components make up the suggested method in this study. This study made use of the DRIVE (digital retinal images for vessel extraction) and STARE (structured analysis of the retina) datasets. Performance metrics including accuracy, specificity, and sensitivity were chosen in order to show the effectiveness of the methodology. The segmentation of blood arteries using the proposed framework in this study achieves higher accuracy than the previous ones.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India

    M. Padmapriya

  2. Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India

    S. Pasupathy

  3. Department of Information Technology, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu, India

    V. Punitha

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Padmapriya, M., Pasupathy, S. & Punitha, V. Early diagnosis of diabetic retinopathy using unsupervised learning. Soft Comput 27, 9093–9104 (2023). https://doi.org/10.1007/s00500-023-08418-z

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