Classification of fundus images for diabetic retinopathy using artificial neural network

People with diabetes may suffer from an eye disease called Diabetic Retinopathy (DR). This is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (i.e retina). Fundus images obtained from fundus camera are often imperfect; normally are in low contrast and blurry. Hence, causing difficulty in accurately classifying diabetic retinopathy disease. This study focuses on classification of fundus image that contains with or without signs of DR and utilizes artificial neural network (NN) namely Multi-layered Perceptron (MLP) trained by Levenberg-Marquardt (LM) and Bayesian Regularization (BR) to classify the data. Nineteen features have been extracted from fundus image and used as neural network inputs for the classification. For analysis, evaluation were made using different number of hidden nodes. It is learned that MLP trained with BR provides a better classification performance with 72.11% (training) and 67.47% (testing) as compared to the use of LM. Such a finding indicates the possibility of utilizing BR for other artificial neural network model

Comparing Performances of Logistic Regression, Classification & Regression Trees and Artificial Neural Networks for Predicting Albuminuria in Type 2 Diabetes Mellitus

In this study, performances of classification methods were compared in order to predict the presence of albuminuria in type 2 diabetes mellitus patients. A retrospective analysis was performed in 266 subjects. We compared performances of logistic regression (LR), classification and regression trees (C&RT) and two artificial neural networks algorithms. Predictor variables were gender, urine creatinine, weight, blood urea, serum albumin, age, creatinine clearance, fasting plasma glucose, post-prandial plasma glucose, and HbA1c. For validation set, the best classification accuracy (84.85%), sensitivity (68.0%) and the highest Youden index (0.63) was found in the MLP model but the specificity was 95.12%. Additionally, the specificity of all the models was close to each other. For whole data set the results were found as 84.21%, 53.95%, 0.50 and 96.32% respectively. Consequently, the model had the highest predictive capability to predict the presence of albuminuria was MLP. According to this model, blood urea and serum albumin were the most important variables for predicting the albuminuria. On the basis of these considerations, we suggest that data should be better explored and processed by high performance modeling methods. Researchers should avoid assessment of data by using only one method in future studies focusing on albuminuria in type 2 diabetes mellitus patients or any other clinical condition