Deep learning: diabetic retinopathy detection using fundus image - Beh, Jun Yue (2025)
Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. DR normally come with diabetes when it affects an individual. Since DR is one of the leading causes that cause blindness, early detection of it is much essential to prevent vision loss. However, the traditional method of diagnosing DR is to rely on manual examination of retinal images by ophthalmologists. It is a time-consuming and is highly prone to error process since it is done by using manpower. Nowadays, with the growing number of diabetic patients, there is an urgent need for an efficient and automated solution for DR screening to prevent blindness due to DR, and therefore this project is proposed. This project is aimed to develop a deep learning-based system using Convolutional Neural Networks (CNNs) for the automated detection and classification of DR. The dataset that is applied in this project is obtained from Kaggle “Diabetic Retinopathy 224 x 224 Gaussian-Filtered” dataset, which consist high-resolution fundus images across the five classes: No DR, Mild, Moderate, Severe and Proliferate DR. Due to the significant class imbalance appear in the dataset, some data augmentation techniques such as flipping, rotation, and zooming, along with class weighting are applied to improve the performance. A DenseNet-201 model with transfer learning from ImageNet was employed, enhanced with global average pooling, batch normalization, dropout, and a softmax output layer. Some metrics were used to evaluate the performance of the model such as accuracy, precision, recall, F1- score and confusion matrix analysis. The experiment results show that the proposed DenseNet-201 model achieves strong classification performance and shows promise as a reliable and efficient tool for automated DR screening, supporting early detection and reducing the workload of the ophthalmologists.