An Automated Diabetic Retinopathy Grading Pipeline based on Deep Learning Approaches

Abstract

Early detection is essential for efficient disease treatment and prevention since diabetic retinopathy (DR) is one of the main causes of blindness in the globe. This thesis addresses the value of early detection during the DR stages and the part deep learning plays in creating a reliable detection system. The study stresses the value of early DR stage detection to stop disease development and enhance patient outcomes. Early illness detection and classification allow for the implementation of prompt therapies that can halt or delay the disease's course. In addition to increasing the likelihood that visual function will be preserved, this lessens the need for expensive and intrusive therapies, which eases the strain on healthcare systems. To achieve accurate detection of DR, a deep learning approach is employed. Specifically, an ensemble method of two Vision Transformer (VIT) models followed by Support Vector Machine (SVM) is utilized. We have used Kaggle APTOS 2019 dataset. The ensemble model capitalizes on the strengths of multiple VIT models to enhance feature representation and improve classification performance.The experimental results demonstrate the effectiveness of the proposed model, achieving an overall accuracy of 91.14%, precision of 92%, recall of 91%, and an F1 score of 91% on APTOS dataset. These results outperform existing methods and validate the potential of deep learning in DR detection. The developed model showcases the capability of deep learning algorithms to provide reliable and efficient diagnostic support, enabling early intervention and ultimately contributing to the prevention of DR-related visual impairment.