Abstract:
Diabetic retinopathy is a condition where high blood sugar damages the retina's blood
vessels, potentially leading to vision loss or blindness. However, manual classification
of diabetic retinopathy severity levels has challenges due to subjectivity and time
constraints and this leads to a more efficient approach. This study, notably the first
initiative, aims to address the classification challenges by using CNNs, as well as a
prototype of a custom-designed CNN along with deep learning and image processing
techniques to classify five severity levels such as Normal, Mild, Moderate, Severe, and
Proliferative Diabetic Retinopathy. This study utilized APTOS 2019 grayscale images
tailored for diabetic retinopathy severity assessment, ensuring a robust evaluation of
each model's performance. Preprocessing techniques include image normalization,
resizing, and augmentation for increasing the robustness and generalization of the
models, followed by data pre-processing to ensure dataset quality and consistency. The
dataset was divided into 80 %, 10%, and 10 % for the training, testing, and validation,
respectively. On the DR dataset, we fine-tune these models based on CNNs using
architecture VGG16. The trained model achieved a test accuracy of 90 % in classifying
the five-severity level of the diabetic retinopathy. The automated classification system
presents significant advancements by contributing to the sustainable management of
diabetic retinopathy, helping to reduce the risk of blindness in diabetic patients.
