Application of Convolutional Neural Networks for Automated Diabetic Retinopathy Detection from Retinal Images

Diabetic Retinopathy (DR) is a debilitating consequence of diabetes that leads to blindness if not diagnosed in its early stages. With the surge of deep learning methodologies, automatic detection of DR has become a highly researched area with promising results. This study presents a comprehensive approach to detect DR employing a tailored Convolutional Neural Network (CNN) model on retinal fundus images. Our proposed model follows a sequential pattern comprising multiple layers for feature extraction and classification. Each convolutional layer utilizes learnable filters to recognize significant patterns and enhance feature representation through non-linear activation functions such as ReLU. The architecture also incorporates pooling layers to reduce spatial dimensions, thereby improving computational efficiency and fully connected layers to integrate learned features. The output layer, fitted with a softmax activation function, consists of neurons equivalent to the number of target classes for DR detection: normal, mild, moderate, severe, and proliferative DR. Our model's weights and biases are optimized using the Adam algorithm during training. This research provides a robust model for automated and accurate detection of DR stages, aiming to augment ophthalmic healthcare services, especially in regions with scarce expert resources. Preliminary results demonstrate the effectiveness of our model, achieving competitive performance on the benchmark dataset. Further, our study paves the way for more advanced research in automated DR diagnosis using deep learning methodologies.