Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

TL;DR

This paper introduces a semi-supervised graph learning approach for diabetic retinopathy detection that leverages both labeled and unlabeled data, improving accuracy and robustness in medical image analysis.

Contribution

The paper presents a novel semi-supervised graph learning algorithm specifically designed for diabetic retinopathy detection, addressing data scarcity and class imbalance issues.

Findings

Significant improvements in classification accuracy, specificity, and sensitivity.

Robustness against noise and outliers demonstrated.

Effective handling of imbalanced datasets in medical imaging.

Abstract

Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.