Diabetes retinopathy classification using Deep Learning Technique

Diabetic retinopathy (DR) is a prevalent complication of diabetes mellitus, leading to progressive vision impairment if left undiagnosed or untreated. This research focuses on advancing the accuracy and efficiency of DR classification through deep learning techniques, specifically employing DenseNet classifiers. The methodology begins with the acquisition of retinal images, followed by standardizing dimensions through resizing and conversion to grayscale to streamline computational processing. Advanced preprocessing steps include noise removal and contrast enhancement to optimize image clarity, crucial for accurate feature extraction. Segmentation techniques further refine the images, isolating regions indicative of DR severity levels. Augmentation strategies such as vertical and horizontal flips, along with random rotations, enhance the robustness of the model by diversifying the training dataset. The processed images are then inputted into a DenseNet classifier, renowned for its ability to capture intricate features across multiple layers, facilitating precise classification into five distinct stages: Mild DR, Moderate DR, No DR, Proliferative DR, and Severe DR. Evaluation metrics focus on accuracy, ensuring reliable and automated detection of DR stages critical for timely clinical intervention. This study contributes to the ongoing efforts in leveraging advanced machine learning techniques for early and accurate diagnosis of diabetic retinopathy, aiming to mitigate the risks associated with vision loss in diabetic patients and improve overall healthcare outcomes.

Keywords: Diabetic Retinopathy Dataset, Pre-Processing, DenseNet, Deep learning, Classification, Accuracy.