Classification of Suspected Liver Metastases Using fMRI Images: A Machine Learning Approach
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Brain fMRI processing and classification based on combination of PCA and SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Decoding visual brain states from fMRI using an ensemble of classifiers
Pattern Recognition
A supervised clustering approach for fMRI-based inference of brain states
Pattern Recognition
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The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods.