## 1 Introduction  
Due to the changes in the environmental conditions in which we currently live, we observe that more people suffer some form of cancer [1]. For example, ultraviolet radiation (UV) is one of the primary risk factors for skin cancer. In 1975, [PERSON] proposed a scale from I to VI of how the skin type reacts to UV [1]. Type I are the very fair skins (more susceptible to developing some form of skin cancer), and Type VI comprises intensely pigmented dark brown skins (less affected). Hence, this kind of illness is most frequent in countries with a predominantly fair skin population. Nowadays, in Ireland, the skin cancer's diagnosis is over 11,000 cases per year. Most people living in Ireland have fair skin, which means skin type I or II on the Fitzpatrick scale. Ireland's government, concerned about the rising incidence of skin cancer, in its National Cancer Strategy (2019-2022), prioritizes the need to implement a national skin cancer prevention plan [2]. In the USA annually, skin cancer diagnosis surpasses five million cases [3]. Globally, in 2010, skin diseases were the fourth leading cause of nonfatal illnesses that caused economic losses due to inabilities [4].  
Abnormal growth of skin cells could develop skin cancer. One of the first warning signs is that skin lesions, like new moles, bumps, sores, scales, or dark spots, grow and do not go away.  
Mainly, skin cancer divides into two categories, melanoma and non-melanoma (basal cell carcinoma and squamous cell carcinoma) [5]. The dermatologists employ the ABCDE criteria in the diagnosis of skin lesions: asymmetry (A), border (B), color (C), differential structure (D), and evolving (E) [6]. In the asymmetry feature, the dermatologist searches if the lesion is uneven. The border characteristic measures the ragged edges. The color analysis determines if the spot has an unusual coloration. The diameter parameter measures if the spot is more extensive than one-quarter inch. The evolving features indicate if the lesion is changing in size, color, or shape. Therefore, the dermatologist analyses the form, appearance, and color of the mole or spot. Detection of skin diseases is one of today's priority tasks worldwide. Characterizing the role that the textures play in skin diseases will help us develop more robust computer-aided diagnosis methods.  
In recent years, neural networks have once again boomed in a wide variety of applications. By obtaining satisfactory results, these become a promising tool for the classification of skin lesions. CNNs are based on the convolution operation of the image with different filters, offering extensive and varied information. In its first layers, CNNs get the lines and edge features, while in the deeper layers, they find more specific details in the images. It is well known that the convolution operation is not invariant to geometric transformations like translation, rotation, and scale [7]. That is why neural networks require a large number of training images to extract all the possible ways that the image can have, hence their high computational cost. In addition, images of skin lesions can have low contrast between the lesion and healthy skin, which could interfere with the correct segmentation of the lesions [8]. That can lead to loss of valuable information that could be used for proper classification of the injury. Due to these aspects, in this work, it is proposed to complement the Densenet-201 network's information with the compact information obtained from fractal signatures, which extract global and local information from the images considering the three color bands, R, G, B.