Detecting Diabetic Retinopathy using Deep Learning

Authors

  • V. K. Bairagi, Faaris Shaikh, Parmeshwar Randive, Sujeetsing More, Mrinai M. Dhanvijay, Priyanka Tupe-Waghmare

Keywords:

Diabetic, Retinopathy, CNN, vision loss.

Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults worldwide, and timely diagnosis is crucial for preventing blindness. Current methods for diagnosing DR rely on manual grading of retinal images, which is time-consuming and prone to inter-observer variability. The development of DR is a complex process involving multiple cellular and molecular pathways, including inflammation, oxidative stress, and vascular dysfunction. Despite its significant impact on public health, there is currently no effective treatment for DR that can halt or reverse its progression. Recent advances in deep learning and image processing have opened up new possibilities for automating the detection of DR. The aim of this study is to develop a system that can accurately classify individuals suffering from diabetic retinopathy. Filtering algorithm is used to clean and preprocess the images collected by users, thereby ensuring the accuracy of the results and reducing the impact of noise on the diagnostic process. An efficient custom three layer CNN model with hyper-parameter tuning is used on kaggle ‘ilovescience’ dataset which gives promising accuracy of 94.45%.

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Published

16.06.2024

How to Cite

V. K. Bairagi. (2024). Detecting Diabetic Retinopathy using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 399–407. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6227

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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