Ten lectures on wavelets
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Structural hidden Markov models for biometrics: Fusion of face and fingerprint
Pattern Recognition
Computer aided diagnosis of ECG data on the least square support vector machine
Digital Signal Processing
Computers in Biology and Medicine
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
Computer aided diagnosis of alzheimer's disease from MRI brain images
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
A Classifier to Detect Tumor Disease in MRI Brain Images
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A decision-making algorithm for automatic flow pattern identification in high-speed imaging
Expert Systems with Applications: An International Journal
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This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.