## 1 Introduction
Then the in-focus reflected light is collected by the detector with the help of the second pinhole. Presently, this tool has greater diagnostic accuracy compared to dermoscopy, both for melanocytic and for non-melanocytic skin tumors [11; 12; 13]. Unlike the previous modalities, RCM remains expensive, although the number of users continues to increase [14] and recent developments have led to a degree of improvement in the portability of RCM devices [15]. By contrast, relatively fewer studies have been published on the RCM modality by use of computer vision techniques, despite their promising results in the clinical context. The use of artificial intelligence could be particularly useful for RCM images because their evaluation by dermatologists is time-consuming. Indeed, unlike with dermoscopy, many RCM images need to be acquired for each skin lesion. Many studies of computer vision focus on understanding these images by predicting the position in the skin layer [16; 17] using stacks of 3D data provided by this modality. A number of other studies have described the structural components of the skin [18] and few of them have classified pathologies based on that specific modality. One of these studies [19] is quite similar to the problem explored in this work, and it suggests several image descriptors for undertaking a classification task. These authors introduced two methods using frequency representation: the first one based on Fourier transform and the second one based on wavelet representation by use of Daubechies 4. The idea behind spectral representation is to extract information at different frequency levels, thereby yielding information about smoothness or complex structures inside the images, and to be consistent with rotations and translations as the pathologies are non-oriented. The second category of descriptors that they employed was based on spatial features by use of gray level histogram (GLH) and gray level co-occurrence matrix (GLCM) statistical descriptors. The statistical descriptors computed from GLCM had been derived from previous work [20]. Finally, the authors proceeded with the classification of images by using different image parts at multiple sizes and they proceeded to classification and regression trees (CART) classification. In a two-class situation, this paper reached an accuracy of 0.96 for the detection of nevi and 0.97 for melanoma pathologies by applying a wavelet extraction and CART classification on a subpart of the 256 \(\times\) 256 pixel images. Another paper is more relevant, as the authors followed the same purpose as this work by suggesting a way to classify solar lentigo pathologies by use of the previous wavelet decomposition, and by fitting the decomposition values to a generalized Gaussian distribution (GGD) to reduce the number of variables. They also suggested that only one variable and only one scale decomposition are relevant for solar lentigo detection. This method was applied to 45 subjects with healthy skin or solar lentigo, and it achieved a sensitivity of 0.81 and a specificity of 0.83 [21]. The scope of this work is to detect malignant tumors and particularly lentigo maligna/lentigo maligna melanoma (LM/LMM) (the most common type of facial melanoma) in RCM images and to help specialists reach a diagnosis based on these images at the patient-level. The previous feature extraction methods and extraction through different convolutional neural network (CNN) architectures were investigated first. As wavelet decomposition and reduction through GGD [21] were shown to be irrelevant in our data context [22], this work did not focus on any of these methods. A comparison of several classification models on full-size images was then carried out to estimate the relevance of these methods. Also, this study will employ the term \"lentigo maligna\" instead of the term \"melanoma in situ type lentigo maligna\" as it was used by the last RCM clinical studies [23; 24; 25; 26] and for the sake of simplicity. The following parts of this paper are organized as follows.