## 1 Introduction
In addition, a major problem has been the lack of a standardized protocol that regularize such a challenges' administration procedure. It would therefore be desirable to develop an automated classification approach able to differentiate between PRP, SSc, and HC, thus limiting the human intervention and without the administration of a cold stimulus. In this perspective, machine learning approaches are valuable to minimize or avoid the clinician's intervention and to speed up diagnosis. Indeed, such approaches are suitable for solving complex task and limiting human intervention. Presently, machine learning algorithms are gaining popularity across a wide range of innovative applications, such as smart houses [20] and autonomous vehicles [21] but also in physiological signal classification [22; 23] or to support disease diagnosis [24; 25]. Machine learning approaches can be feature-based or data-driven [26]. In the traditional feature-based machine learning approach, features are manually selected and extracted. The difficulty with this approach is that it is necessary to choose which features are important and, as the number of classes to classify increases, feature extraction can become cumbersome. On the other hand, data-driven methods provide a principled set of mathematical methods for extracting meaningful features from data [27]. Specifically, it learns from and makes predictions based on data. Those approaches are usually performed by employing advanced machine learning algorithm such as a deep neural network. Indeed, a deep neural network model exploits multiple layers of nonlinear information processing for feature extraction and transformation as well as for pattern analysis and classification [28]. Figure 1 shows the difference between the data driven and feature-based methods. With respect to a deep neural network, deep convolutional neural networks (DCNNs) have become the leading architecture for most image classification tasks [29]. DCNNs make use of kernels (also known as filters), to detect features throughout an image. A kernel is a matrix of values, called weights [28]. They are typically composed of three types of layers: convolution, pooling, and fully connected layers. The first two perform feature extraction, whereas the third maps the extracted features into a final output, which is often a classifier. DCNNs find complex relationships by minimizing a cost function (a measure of error between the DCNN and real outputs) through the use of gradient descent approaches and a backpropagation algorithm [30]. DCNNs have been widely used in biomedical images analysis and they have reached excellent classification outcomes [31; 32]. In this study, a novel automated DCNN classification methodology that aims to distinguish among PRP, SSc, and HC based on patient's hand thermal patterns measured at rest (i.e., without the administration of a cold challenge) is presented. The performances of the proposed classifier were compared to that of a DCNN model implemented to classify hands thermal images of participants undergoing the cold challenge procedure (CCP). Moreover, for both the analyses, the DCNN models' performances were compared to those of a feature-based machine learning approach. The comparison between different models allowed us to investigate the capability of the DCNN classifier based on the thermal image at rest to identify PRP, SSc, and HC with respect to approaches that require the administration of a cold challenge and the intervention of an expert operator.