Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Automated diagnostic systems: arterial disorders detection
ACS'08 Proceedings of the 8th conference on Applied computer scince
Advances in automated diagnostic systems
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Computers in Biology and Medicine
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A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals. Decision making was performed in two stages: feature extraction by the eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the Doppler ultrasound signals by the combination of eigenvector methods and the classifiers. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the Doppler ultrasound signals and the probabilistic neural networks (PNNs), recurrent neural networks (RNNs) trained on these features achieved high classification accuracies.