Publicaciones en las que colabora con JAVIER RAMÍREZ PÉREZ DE INESTROSA (142)

2022

  1. Evaluating Intensity Concentrations During the Spatial Normalization of Functional Images for Parkinson’s Disease

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Quantifying Differences between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images

    International Journal of Neural Systems, Vol. 32, Núm. 5

  3. Temperature Control and Monitoring System for Electrical Power Transformers Using Thermal Imaging

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2019

  1. Classification Improvement for Parkinson’s Disease Diagnosis Using the Gradient Magnitude in DaTSCAN SPECT Images

    Advances in Intelligent Systems and Computing

  2. Comparison Between Affine and Non-affine Transformations Applied to I [ 123 ] -FP-CIT SPECT Images Used for Parkinson’s Disease Diagnosis

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Comparison between affine and non-affine transformations applied to I[123]-FP-CIT SPECT images used for Parkinson’s disease diagnosis

    Understanding the Brain Function and Emotions: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019 Almería, Spain, June 3–7, 2019 Proceedings, Part I (Springer Suiza), pp. 379-388

  4. Deep Convolutional Autoencoders vs PCA in a Highly-Unbalanced Parkinson’s Disease Dataset: A DaTSCAN Study

    Advances in Intelligent Systems and Computing

  5. Isosurface Modelling of DatSCAN Images for Parkinson Disease Diagnosis

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  6. Parkinson's disease detection using isosurfaces-based features and convolutional neural networks

    Frontiers in Neuroinformatics, Vol. 13