Juan Manuel Górriz

TL;DR: This work used dual-tree complex wavelet transform to extract features and multinomial logistic regression was employed to be the classifier, which performed better than five state-of-the-art methods.

TL;DR: This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases.

TL;DR: This work presents a complete computer-aided diagnosis (CAD) system for the early diagnosis of the AD based on multivariate approaches that yields better accuracy values than other recently developed techniques.

TL;DR: This paper evaluates different pattern classifiers including k -nearest neighbor (k NN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis (CAD) system for improving the early detection of the AD.

TL;DR: The experimental results showed that the proposed novel method for automatic classification of magnetic resonance images (MRI) based on independent component analysis (ICA) can successfully discriminate AD and MCI patients from NC subjects.