Identification of diabetic retinopathy using convolutional neural network

Nida Nasir; Feras Barneih; Omar Alshaltone; Mohammad AlShabi; Ahmed Al Shammaa

doi:10.1117/12.2663998

14 June 2023 Identification of diabetic retinopathy using convolutional neural network

Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, Ahmed Al Shammaa

Proceedings Volume 12548, Smart Biomedical and Physiological Sensor Technology XX; 125480H (2023) https://doi.org/10.1117/12.2663998
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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Abstract

Due to high blood sugar levels, diabetic retinopathy (DR), a complication of diabetes, affects the retina in the back of the eye. It may cause blindness if undiagnosed and mistreated. The early detection and treatment of DR are made easier by retinal screening. This paper proposes using an image-based dataset to build different convolution neural network (CNN) models to detect DR in its early stages to ease the screening procedure. The accuracy achieved was 0.9615 using the VGG model and 0.9712 using the Inception-ResNet model. This study demonstrates the effectiveness of using deep learning techniques to aid in diagnosing and predicting diabetic retinopathy.

Citation Download Citation

Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, and Ahmed Al Shammaa "Identification of diabetic retinopathy using convolutional neural network", Proc. SPIE 12548, Smart Biomedical and Physiological Sensor Technology XX, 125480H (14 June 2023); https://doi.org/10.1117/12.2663998

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