Automated Detection of Proliferative Diabetic Retinopathy Using Brownian Motion Features
Diabetes is a chronic disease caused when the body does not produce enough insulin or the insulin produced fails to break down glucose in the blood. It is a non-communicable disease and the condition is irreversible. Treatment is vital to prevent the condition from worsening and complications.
One of the complications of diabetes is diabetic retinopathy, a disease that affects the vision. There are four stages of diabetic retinopathy. In this paper, we focus on the last stage of diabetic retinopathy, which is Proliferative Diabetic Retinopathy (PDR). Fractal dimensions and Hurst
coefficients are the features extracted from normal and proliferative diabetic retinopathy images. These features are then input to five classifiers namely, Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Decision Tree (DT), K-Nearest Neighbour (KNN) and Fuzzy Sugeno
(FS) to select the best classifier. FS classifier yielded the highest average accuracy of 94%, sensitivity of 92% and specificity of 96%.
Keywords: BROWNIAN MOTION; FRACTAL DIMENSION; FUZZY SUGENO CLASSIFIER; PROLIFERATIVE DIABETIC RETINOPATHY
Document Type: Research Article
Publication date: 01 April 2014
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content