C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast learning algorithm for deep belief nets
Neural Computation
Letters: Fully complex extreme learning machine
Neurocomputing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
A novel approach to protein structure prediction using PCA or LDA based extreme learning machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Using hybrid neural networks for identifying the brain abnormalities from MRI structural images
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.