# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 3 Materials and Methods
### Methods
In contrast to ResNet, it explicitly differentiates the residual information preserved from the new information added to the network [58]. The intrinsic merits of the model include the reduction in the required hyperparameters by ignoring redundant feature maps and alleviating the vanishing gradient problem. Four types of DenseNet that were examined in this study are DenseNet-121, DenseNet-161, DenseNet-169, and DenseNet-201 [59]. DPN reforms the skip connection and inherits the intrinsic merits of feature reuse from a residual network (ResNet) and feature exploration from a densely connected network (DenseNet). These attributes make DPNs have good parameter efficiency, low computational cost, and low memory footprint. Five variations of DPN that were examined in this study are DPN-68, DPN-92, DPN-98, DPN-107, and DPN-131 [60]. The inception architecture combines the strength of the network in a network (NiN) with a repeated block paradigm. The basic convolutional block in the inception architecture is called the inception block. In this study, a variation of the inception architecture termed inception-ResNet-v2 that utilizes the residual connections was used to achieve an improved classification performance [64]. The ReXNet uses a search method for channel parameterization through the piecewise linear functions of the block index [65]. The two types of ReXNet examined in this study are ReXNet_1.5 and ReXNet_2.0, each corresponding to \(x1.5\) and \(x2.0\) repeated layers, respectively. Public lesion image datasets were used for the training and validation of base learners to support a two-staged training process. The individual trainable input image was resized to 224 \(x\) 224, while each batch of data was fed as input to a learner. In the first training stage, a balanced 2000 data subset of benign and malignant classes consisting of randomly selected 1000 benign and 1000 malignant images were used. In the training, 800 benign and 800 malignant images were used, while 200 benign and 200 malignant images were used for cross-validation. In the second training stage, the entire 58,367 multiclass skin lesion images were used of which about 80.00% of the entire dataset was for training, while the remaining 20.00% of images were used to validate the discriminating capability of each learner. The purpose of the stage 1 evaluation was to identify the base learners with quality performance on ground truth segmented images or non-segmented images to improve the overall classification performance of skin lesions. This is because there is no consensus reached in the literature on the effect of image segmentation on performance of classification algorithms. Some studies have used segmentation as a prerequisite for performing skin lesion classification [40; 75; 76], while other have either argued against its necessity or completely ignored its usage for lesion classification [52; 77; 78]. Consequently, an experiment was performed at Stage 1 with a control group of unsegmented skin lesion imagesand an experimental group of ground truth segmented skin lesion images. The 26 investigated deep learning algorithms were trained separately with experimental group images and control group images. The base learners that recorded a minimum dice coefficient (\(DSC\)) of 0.92 were selected to participate in the Stage 2 evaluation. This was to avoid the potential side effect of imbalanced accuracy and to select deep learners with a consistent discriminating ability across different images. In stage 2, each base learner was trained to learn the discriminating attributes of AKIEC, BCC, DF, MEL, NV, SK, SCC, and VASC skin lesions. In addition, each learner was trained to classify a skin lesion as either BKL or INDB. A skin lesion is classified as BKL if it has keratosis-like features but cannot be recognized as belonging to a specific known keratosis class. Moreover, a lesion is categorized as INDB if it has benign features but cannot be discriminated from a specific known class.