## 1 Introduction  
Skin cancer is the most common cancer in the United States and worldwide [1]. Although the majority of the works in the literature are focused on melanoma detection, the most common malign skin lesion is the non-melanoma skin cancer (NMSC). Over 95% of NMSC cases are Basal Cell Carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC) [2]. Specifically, BCC has an incidence higher than 70% [3] among all skin cancer, it has the best validated clinical criteria for its diagnosis [4], and it presents the higher variability in the presence of these dermoscopic criteria. The detection of NMSC can be performed by visual inspection by a skilled dermatologist, but there are many benign lesions that can be confused with NMSC, leading to unnecessary biopsies, in a proportion of five biopsies versus one actual cancer case [5]. The increase in the incidence of BCC is provoking an overload for dermatologists. In the Andalusian Health System, teledermatology is being implanted. Nowadays, 315 demands of teledermatology consultation per month are received and 210 receive diagnostic criteria of BCC. Thus, a Computer Aided Diagnosis (CAD) tool that assists general practitioner physicians and provides a prioritization in the teledermatology consultation would have great utility. Different kinds of images have been traditionally used in order to classify NMSC automatically (spectroscopy, optical coherence tomography, etc.). However, the simplestone and most used is the digital dermoscopy, that is, a digital color photograph, enhanced by a dermoscope. Lately, and due to the availability of databases due to the challenges proposed by ISIC [6], the use of artificial intelligence methods, and in particular, the use of deep learning neural networks, have become very popular in dermatology. In this sense, this paper is focused on machine learning algorithms, in particular, deep learning ones, and using dermatoscopic images from ISIC challenges [6]. Most of the works published have been focused on melanoma segmentation and classification [7, 8]. On the contrary, much less work has been devoted to NMSC detection. [PERSON] et al. performed a systematic analysis of existing methods for automatic detection of NMSC in 2019. They came to the conclusion that, although most of the methods attain an accuracy similar to the reported diagnostic accuracy of a dermatologist, all the methods require a clinical study to assess the validity of the methods in a real clinical scenario [9]. There are three methods that attain the best classification metrics according to this study. [PERSON] et al., in 2017, reached 100% in all the metrics but the test set was only 10 images [10]. The same authors, in 2018, tested their methods with an extended database, obtaining the same results [11]. [PERSON] et al. also achieved 100% sensitivity, but their specificity was 12% [12]. [PERSON] et al. applied deep neural networks to differentiate between BCC, SCC and benign lesions. They achieved an AUROC score of 0.997, 1 and 0.998, respectively [13]. [PERSON] et al. analyzed the performance of a deep learning-based application for the diagnosis of BCC, as compared to dermatologist and non-dermatologist physicians [14]. In [PERSON] et al., 12 skin diseases were classified, employing a deep learning algorithm. They used three databases and concluded that the tested algorithm performance is comparable to that obtained by 16 dermatologists. One of these skin diseases is BCC [15]. Following this comparison, [PERSON] et al. [16] and [PERSON] et al. [17] also proposed methods based on deep learning to perform a multiclassification of skin diseases. [PERSON] et al. proposed an ensemble approach and compared it with the original Densenet-121, obtaining a better performance. [PERSON] et al. [18] tested two market-approved tools, one employed a Machine Learning (ML) technique and one is based on Convolutional Neural Networks (CNN). Although they tested 1981 skin lesions, only 28 lesions were BCC.