Approximation capabilities of multilayer feedforward networks
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Top-Down and Bottom-Up Strategies in Lesion Detection of Background Diabetic Retinopathy
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Computer Methods and Programs in Biomedicine
Template matching algorithm for exudates detection from retinal fundus images
International Journal of Computer Applications in Technology
Hi-index | 0.00 |
Diabetic retinopathy (DR) is an important cause of visual impairment in developed countries. Automatic recognition of DR lesions in fundus images can contribute to the diagnosis of the disease. The aim of this study is to automatically detect one of these lesions, hard exudates (EXs), in order to help ophthalmologists in the diagnosis and follow-up of the disease. We propose an algorithm which includes a neural network (NN) classifier for this task. Three NN classifiers were investigated: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM). Our database was composed of 117 images with variable colour, brightness, and quality. 50 of them (from DR patients) were used to train the NN classifiers and 67 (40 from DR patients and 27 from healthy retinas) to test the method. Using a lesion-based criterion, we achieved a mean sensitivity (SE"l) of 88.14% and a mean positive predictive value (PPV"l) of 80.72% for MLP. With RBF we obtained SE"l=88.49% and PPV"l=77.41%, while we reached SE"l=87.61% and PPV"l=83.51% using SVM. With an image-based criterion, a mean sensitivity (SE"i) of 100%, a mean specificity (SP"i) of 92.59% and a mean accuracy (AC"i) of 97.01% were obtained with MLP. Using RBF we achieved SE"i=100%, SP"i=81.48% and AC"i=92.54%. With SVM the image-based results were SE"i=100%, SP"i=77.78% and AC"i=91.04%.