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Juan Manuel Górriz

Researcher at University of Granada

Publications -  403
Citations -  8595

Juan Manuel Górriz is an academic researcher from University of Granada. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 43, co-authored 360 publications receiving 6429 citations. Previous affiliations of Juan Manuel Górriz include University of Cádiz & University of Cambridge.

Papers
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A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database

TL;DR: Two multivariate approaches that use different methodologies to relieve the small sample size problem are compared and the validity of both methods is tested by implementing several CAD systems with linear and nonlinear classifiers and comparing them with previous approaches such as VAF and PCA.
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Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images

TL;DR: In this paper, the authors compared two alternative versions of the Gauss-Newton method with an additional parameter, which allows the adaptive change of the step length along the descent direction.
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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

TL;DR: In this paper, the authors provided various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals and compared the obtained results with those of conventional intelligent methods.
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A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling

TL;DR: A novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD) is proposed, a 14-way data augmentation to enhance the training set, and stochastic pooling to replace traditional pooling methods are introduced.
Proceedings ArticleDOI

Automatic computer aided diagnosis tool using component-based SVM

TL;DR: This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the Alzheimer's disease based on a first automatic feature selection, and secondly a combination of component-based support vector machine (SVM) classification and a pasting votes technique of ensemble SVM classifiers.