## 2 Materials and Methods
### Feature Extraction Methods  
In order to classify the data, a reduction of the image information had to be performed in a new feature space able to distinguish \"malignant\" from \"benign\" image types. According to the dermatologists, texture plays an important role in the differentiation of tissue types. The first part of this section focuses on handcrafted feature extractors based on texture from previous work [19]. The deep extraction methods applied to this context and inspired by a previous work on dermoscopy images [6] are then detailed. All of the feature extraction methods are listed in Table 1, and the next parts follow this table structure. The \"Spatial\" extraction methods were based on spatial patterns of pixels, by use of GLH and GLCM. The method called \"Haralick\" refers to previous work based on texture features [20] and it uses the GLCM concept by computation of the twelve statistical characteristics listed in Table 2--GLCM \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Name** & **Number of Features** \\ \hline \multirow{2}{*}{Spatial} & Haralick & 12 \\ \cline{2-3}  & GLH + GLCM & 17 \\ \hline \multirow{2}{*}{Frequency} & Fourier & 38 \\ \cline{2-3}  & Wavelet & 39 \\ \hline \multirow{4}{*}{Transfer Learning} & VGG-16 & 512 \\ \cline{2-3}  & Inception-V3 & 2048 \\ \cline{1-1} \cline{2-3}  & ResNet & 2048 \\ \cline{1-1} \cline{2-3}  & Inception-ResNet & 1536 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The list of all of the feature extraction methods performed in this paper and their associated extracted number of features. Figure 2: This figure shows several examples of images related to the three different types of tissue: namely “healthy”, “benign” and “malignant”. For this work purpose, the “healthy” and “benign” labels are merged under the “benign” label. Features column. These characteristics were extracted along horizontal, vertical, and two diagonals. A mean was computed along these axes as the tissues are not oriented in space, and to reduce the number of features. A second method from previous work [19], called \"GLH + GLCM\" in this paper, expanded the first twelve initial characteristics of Haralick and it added five others based on GLH. In total, 17 features were extracted for each image, and all of the statistical properties extracted are listed in Table 2. The Haralick features extraction was performed using the \"Mahotas\" library [28] and histogram feature extraction was computed with help from the \"Scipy\" library [29]. The second category of extraction methods, called \"Frequency\", refers to a set of methods based on frequency approaches. The first method of this category is called \"Fourier\" and is based on the Fourier transform. The main idea is to provide different levels of information as high frequency refers to high-contrast parts and low frequencies to homogeneous areas in the image. As the spectrum is symmetrical around the origin, only half of this spectrum was considered for the sake of computational efficiency. Then, a mean value was computed for all of the coefficients located at the same radial distance from the origin, at 22 different radius sizes between 0 and the diagonal size of the image. A previous paper [30] has also shown the relevance of this method in the context of textural images. Finally, 16 constant directions were taken from the origin of the power spectrum, and a mean value was computed for each of them [19]. The second method of this category is called \"Wavelet\" in this work and is based on Wavelet transform by use of a decomposition based on a Daubechies 4 that provides quite fine localization properties [19]. This decomposition was made at five scales, and only the four last scales were considered to compute coefficients. For each of them, three statistical measures were computed: the standard deviation, the energy, and the entropy.