[6,8,20] Some of the features have different descriptions which a

[6,8,20] Some of the features have different descriptions which are all JAK considered. Color variation group comprises 72 features of RGB and non-RGB color spaces components and gray scale image. Most descriptors of this group are statistical and are extracted from lesion mask which doesn’t contain glows areas. Moreover, pixels with values <70% of the maximum of each channel are removed

in calculations related to a healthy skin to ensure that there is no effect of hairs. Among the statistical characteristics of RGB color space components and gray scale image can be noted to the minimum, maximum, range of values, mean, standard deviation, coefficient of variation and variance and skewness, normalized standard deviation, ratio of mean values of RGB components, six basic colors counters,[6] relative chromaticity.[22] The statistical characteristics

of non-RGB color space components are mean and standard deviation. These spaces include CIElch,[31] CIEl*a*b*,[32] HSI and spherical colour space which is defined by three Br, angle-α and angle-β components.[26] Lesion diameter features contain 7 features of best-fit ellipse diameter, major diameter and the maximum distance between two nonadjacent points on the lesion border. Lesion texture features are extracted from gray level co-occurrence matrixes. These features include mean and range of values of 21 descriptors

which are calculated for each of the four co-occurrence matrix for four different orientations of 0°, 45°, 90° and 135° and overall, describe 42 features for lesion texture. Among the co-occurrence matrixes descriptors can be noted to the auto-correlation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, entropy, homogeneity, maximum probability,[33] variance, sum average, sum entropy, sum variance, difference variance, difference entropy, information measures of correlation,[34] inverse difference, inverse difference normalized and inverse difference moment normalized.[35] Typically, values of extracted descriptors are at different ranges which if set in a certain AV-951 range leads to significantly improved classification performance. For this reason, values of descriptors are normalized using z-score conversion and Eq. 5. In the above equation, fi, j is the amount of j-th descriptor of i-th image and μj and σj are mean and standard deviation of j-th descriptor, respectively. This conversion ensures that 99% of Zi, j values are in the range of zero and one. What is out of this range is rounded to zero or one.[36] Classification After the feature extraction stage, a set of high-dimensional data is obtained which high number of them is effective on the accuracy and required time for accurate classification.

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