## 1 Introduction  
As the incidence rate for skin cancers has steadily increased over the years, they are now the most prevalent form of human malignancy. These diseases affect people in their everyday life, as they have a pronounced social impact on the affected individuals as a result of a decrease in their quality of life as well as because they have the potential to become lethal. In addition, they have significant economic consequences, with an estimated cost of 8 billion dollars per year in the United States [1], and they can stretch the ability of dermatological centers to cater to the at times overwhelming demand for screening and treatment. However, most of these repercussions can be avoided by early detection and appropriate surgeries [1]. Currently, clinicopathological correlation is the gold standard to diagnose skin cancers. The histological examination is relatively time-consuming as it requires excision of the affected area, the embedding of the sample in paraffin, the generation of thin tissue sections that then need to be stained and examined by a histopathologist. Despite its accuracy, this technique remains time-consuming, invasive, and inconvenient for doctors and patients. Consequently, several non-invasive imaging techniques have been developed to help the clinical diagnosis of skin cancers, and some of these are now commonly used by dermatologists. For instance, clinical photography and dermoscopy are both examples of affordable and intuitive techniques that are presently widely used by dermatologists. Dermoscopy tends to replace clinical photography as it significantly improves the quality of the diagnoses made by experts, due largely to the acquisition of high-magnification images of the skin [2]. Research papers on dermatology nowadays tend to focus on the dermoscopy modality used to perform automatic classification of lesions. Most of them obtain acceptable results with melanocytic pathologies [3]. Older methods focus on finding the most pertinent combination of preprocessing steps and handcrafted features to be used in a machine learning scheme [4; 5]. By contrast, most recent methods use deep learning approaches, and they have yielded impressive results in this discipline [6]. In this particular study, the authors used an Inception-V3 architecture pre-trained on the \"ImageNet\" database [7], and they fine-tuned this model on a dataset of 129,450 clinical images containing 2032 different skin lesions and distributed across 757 classes. They carried out the classification at different taxonomy levels, and at the first level of classification (Non-neoplastic versus benign versus malignant), they achieved an accuracy of 0.72 \(\pm\) 0.9 compared to 0.66 on a subset of these data by specialists. However, dermoscopy imaging devices only provide surface and chromatic information. To overcome this limitation, reflectance confocal microscopy (RCM) modality is another type of imaging technique used by dermatologists that provides high-resolution images of the skin on a micrometer scale. Furthermore, this modality can provide structural information at different depths of the skin by adjustment of the wavelength properties and the focal point [8]. The RCM device was first designed by [PERSON] [9]. The principle of this device is to emit and focus a low power laser on a specific point of the skin, then the light from this spot is reflected and collected through an objective and a pinhole that allows only the light from the in-focus plane to reach the detector (see Figure 1). In this situation, the illuminated point and the detector aperture have confocal (a contraction of conjugate focal) planes [10]. The main interest of this focal point/plane is to provide only tissue information from a specifically chosen depth. Different factors can affect the depth: illumination wavelength, illumination power, reflective and scattering properties of the skin. Figure 1: Principle of the RCM designed by [PERSON] [9]. The light source is transmitted through a pinhole and focused on the sample through an objective.