Diabetic Retinopathy Classification from Retinal Images using Machine Learning Approaches
This work addresses early detection of diabetic retinopathy to prevent blindness, but it is incremental as it applies standard classifiers without novel methodological contributions.
The paper tackled the problem of classifying diabetic retinopathy stages from retinal images using machine learning, achieving best results with Random Forest at 76.5% accuracy, 77.2% sensitivity, and 93.3% specificity.
Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively.