Assessment of four neural network based classifiers to automatically detect red lesions in retinal images.
Diabetic retinopathy (DR) is an important cause of visual impairment in industrialised countries. Automatic detection of DR early markers can contribute to the diagnosis and screening of the disease. The aim of this study was to automatically detect one of such early signs: red lesions (RLs), like haemorrhages and microaneurysms. To achieve this goal, we extracted a set of colour and shape features from image regions and performed feature selection using logistic regression. Four neural network (NN) based classifiers were subsequently used to obtain the final segmentation of RLs: multilayer perceptron (MLP), radial basis function (RBF), support vector machine (SVM) and a combination of these three NNs using a majority voting (MV) schema. Our database was composed of 115 images. It was divided into a training set of 50 images (with RLs) and a test set of 65 images (40 with RLs and 25 without RLs). Attending to performance and complexity criteria, the best results were obtained for RBF. Using a lesion-based criterion, a mean sensitivity of 86.01% and a mean positive predictive value of 51.99% were obtained. With an image-based criterion, a mean sensitivity of 100%, mean specificity of 56.00% and mean accuracy of 83.08% were achieved.
García
María
M
Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Paseo Belén 15, 47011 Valladolid, Spain. maria.garcia@tel.uva.es
López
María I
MI
Alvarez
Daniel
D
Hornero
Roberto
R
D003116:Color; D003930:Diabetic Retinopathy / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012680:Sensitivity and Specificity