# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 2 Related Studies  
The improved LeNet method that uses adaptive piecewise linear activation as an alternative to the conventional activation function was proposed to improve the classification of melanoma skin lesions [31]. The CNN framework termed Dermo-Doctor was recently developed to detect skin lesions [32]. The framework performs lesion segmentation, uses two encoders, and fuses feature maps from the encoders to obtain the desired classification result. This past work was further expanded in a proposal called DermoExpert using a hybrid of CNNs, class rebalancing, and transfer learning [33]. The classification of skin lesions network (CSLNet) was suggested based on a specialized deep convolutional neural network (DCNN) [34]. The backpropagation algorithm was used to train the DCNN model to evolve the network weights. A leaky rectified linear unit (LeakyReLU) was used as an activation function in the convolution layers, while SoftMax was leveraged as an activation function in the output layer. The L2 normalization scheme was used to regularize the kernel in the dense layer to prevent the possibility of over-fitting, often associated with a learning method. Most classification problems can be generally categorized as unary, binary, or multiclass [35]. Multiclass classification is the processing task of assigning a data sample to exactly one class among three or more classes. The task is particularly challenging for an imbalanced dataset of medical images that present multiple undesirable artifacts such as vignettes, ruler markings, specular reflections, shadows, and hair shafts. Moreover, a skewed dataset distribution can make conventional machine learning algorithms ineffective, especially when predicting examples of a minority class. A multiclass problem is sometimes resolved directly or by further reducing the problem into a series of two-class subproblems. Direct resolution can be achieved with the application of SoftMax or Sigmoid function. The use of either one-vs-one (OvO) and one-vs-rest/one-vs-all (OvR/OvA) schemes can be used to further break a multiclass problem into multiple binary subproblems [36; 37; 38]. Multiple sub-datasets are used for training both OvO and OvA and the prediction of input data is passed through each generated model where a model with the highest probability gives the final prediction. The OvO is a heuristic algorithm that splits a multiclass problem into multiple binary subproblems. The source dataset is divided into one sub-dataset per each class versus every other class to generate \(n(n-1)/2\) binary classification models where n is the number of classes. However, this could lead to countless sub-datasets that might be difficult to scale, especially for large source datasets. The OvA rits a multiclass problem into several binary subproblems by dividing the source dataset into one sub-dataset for each class versus the rest of the classes to generate \(n\) binary classification models. The OvA is less verbose in comparison to the OvO for the required sub-datasets to be created, but it requires the creation of a model for each class. This requirement might be undesirable for large source datasets or multiclass problems with a considerable number of classes. The introduction of deep learning has made a direct resolution of multiclass problems relatively exciting. The present study has followed the ensemble deep learning approach of a direct multiclass resolution using SoftMax to avoid the innate concerns associated with OvO and OvA. Multiclass classification can be a single-label prediction outcome or multi-label in nature. Single-label classification indicates that the outcome of a prediction generates exactly one class. A single SoftMax layer is often used as the last layer for neural networks that require a single-label prediction outcome. However, multi-label classification indicates that the outcome of a prediction generates two or more classes. Multiple sigmoid layers are typically used as the last layer for neural networks that require multiple-label prediction outcomes.