# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 3 Materials and Methods
### Dataset  
This study uses 71,522 images congregated from 6 publicly available datasets of international skin imaging collaborations (ISIC) for experimentation. The training and validation datasets comprise the ISIC 2016 (ISIC_2016_TRN) [66], ISIC 2017 (ISIC_2017_TRN and ISIC_2017_VAL) [4], ISIC 2018 (ISIC_2018_TRN) [67; 68], ISIC 2019 (ISIC_2019_TRN) [4; 67; 69], and ISIC 2020 (ISIC_2020_TRN) [70]. The datasets as described in Table 1 reflected 2, 3, 3, 7, 9, and 9 skin lesion classes respectively. It should be noted that images overlap across different ISIC datasets. Many studies on skin lesion segmentation and classification, for example, transformed lesion image data from original red, green, and blue (RGB) color to other color models such as the international commission on illumination perceptually uniform (CIELab) color model [1; 14; 39; 71] and non-linear hue, saturation, value (HSV) color model [28; 52]. However, the original RGB colors of skin lesion images were preserved in this study to avoid additional computation costs and possible tinting that can result from the specification of an inappropriate illuminant. Figure 1 shows the inclusion and exclusion protocol for certain skin lesion classes. The lesion classes like lentigo not otherwise specified (NOS), solar lentigo, lichenoid keratosis, cafe-au-lait macule, and atypical melanocytic proliferation in ISIC_2020_TRN were excluded because they present less than 50 lesion images. The number of such images in the sub-dataset was 90, thereby reducing the ISIC_2020_TRN experimental images from 33,126 to 33,036. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Descriptions** & **Sizes** & **Classes** \\ \hline ISIC\_2017\_TRN [4] & \begin{tabular}{c} Training set for ISIC 2017 \\ Classification Task \\ \end{tabular} & 2000 & 3 \\ \hline ISIC\_2017\_VAL [4] & \begin{tabular}{c} The validation set for ISIC \\ 2017 Classification Task \\ \end{tabular} & 150 & 3 \\ \hline ISIC\_2016\_TRN [66] & \begin{tabular}{c} Training set for ISIC 2016 \\ Classification Task \\ \end{tabular} & 900 & 2 \\ \hline ISIC\_2019\_TRN [4; 67; 69] & \begin{tabular}{c} Training set for ISIC 2019 \\ Classification Task \\ \end{tabular} & 25,331 & 9 \\ \hline ISIC\_2018\_TRN [67; 68] & \
\begin{tabular}{c} Training set for ISIC 2018 \\ Classification Task \\ \end{tabular} & 10,015 & 7 \\ \hline ISIC\_2020\_TRN [70] &
\begin{tabular}{c} Training set for ISIC 2020 \\ Classification Task \\ \end{tabular} & 33,126 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental image datasets. Figure 2 displays the sample skin lesion images of ten different classes. The benign lesion has a total of seven classes which are actinic keratosis and intraepithelial carcinoma (AKIEC), benign keratosis-like lesions (BKL), dermatofibroma (DF), indeterminate benign (INDB), melanocytic nevi (NV), seborrheic keratosis (SK), and vascular lesions (VASC). The malignant lesion accounted for three classes which are melanoma (MEL), basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). The images used in the dataset contain several noise attributes such as hair shafts (Figure 2a,c,e,f,g,k,l), vignettes (Figure 2a,d,l), ruler markings (Figure 2g,k), air bubble (Figure 2h), blood vessel (Figure 2f,h), and specular reflections (Figure 2e,l). The images equally contain early in-situ skin lesions with difficult border tracing (Figure 2b,c,g,i,j) and invasive skin lesions having different shades of color (Figure 2a,d-f,h,k,l). The use of in-situ lesion skin images can enhance the training of a learner for the early detection of skin diseases. Moreover, the use of invasive skin lesion images can ensure that the proposed ensemble learning methods can provide a valuable second opinion to dermatologists when investigating the diagnosis of a specific skin lesion. It has been earlier mentioned that ISIC datasets contain duplicate images across different datasets.