## 2 State-of-the-Art
### Computer Studies and Algorithms in PD Classification  
MRI analysis for PD classification is a very fruitful area of research; indeed, in recent years, many relevant papers have been published developing novel methodologies [18; 19; 20; 21]. Most of the articles cited in the bibliography focus on the realization of binary classifiers in order to differentiate between control and PD patients. The use of intelligent systems in the field of neuroimaging has shown excellent synergy, developing precise automatic diagnostic systems [12; 14; 22]. For example, in [PERSON] et al. [23], 19 early PD patients and 27 normal patients (volunteers) recruited from Zhejiang University were used for the classification using a SVM classifier. For each patient, resting-state functional magnetic resonance imaging (rsfMRI) and structural images were used. Three different types of characteristics were extracted from the rsfMRI images: ReHo (regional homogeneity), ALFF (amplitude of low-frequency fluctuations) and RFCS (regional functional connectivity strength). For the structural images, the volume characteristics from the gray matter, the white matter and the cerebrospinal fluid were computed. With this reduced data set, the accuracy was 87%, sensitivity was 79% and specificity was 93%. In [PERSON] et al. [24], a multi-class classification system for PD analysis, based on a sparse discriminative feature selection methodology, is presented. The authors presented a framework to create a least square regression model based on [PERSON]'s linear discriminant analysis and locality preserving projection. Input data were acquired from the Parkinson's Progression Markers Initiative (PPMI) database, using 123 PD, 56 normal and 29 SWEDD images and obtaining an accuracy of 78.4%. A methodology based on complex networks for precise early diagnoses using MRI is presented by [PERSON] et al. [18]. The authors defined a network model of brain regions and associate proper connectivity measures to each region, with the brain being characterized through a feature vector encoding the local relations where brain regions interweave. Feature selection and learning is performed by using Random Forests. SVM is used for classification normal (169 patients) and PD (374 patients) from the PPMI database, obtaining an accuracy of 93%. An interesting approach is presented by [PERSON] et al. [25], in which the potential of a set of features obtained from [123]FP-CIT SPECT brain images are used. The authors selected seven features (five associated to uptake ratios on the striatum and two features correlated to the estimated volume and length of the striatal region with normal uptake) from each brain hemisphere. The classification capacities of each individual feature and groups of features were analyzed using a data set of 209 normal and 443 PD patients from PPMI. The best classification performance was achieved using the SVM classifier, using all the features (accuracy = 97.9%, sensitivity = 98% and specificity = 97.6%). In recent years, Convolutional Neural Networks (CNN) have been extensively used in the automatic classification of images using Deep Learning (DL) neural networks and in particular in PD diagnosis [26; 27]. For example, in [PERSON] et al. [27], MR images of Parkinson's disease subjects and healthy control volunteers were classified by a DL neural network, showing an accuracy of 88.9%, based on the PPMI data set. The CNN architecture, AlexNet, is used to improve the diagnosis of PD via the transfer learning methodology. In [28], a deep learning framework for the simultaneous classification and regression of PD patients using personal information (i.e., age, gender) and MRI was proposed. The data set consists of 452 Parkinson patients, including 292 males and 160 females and 204 images from people in healthy conditions (134 males and 70 females).