# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 2 Related Studies
The classification problems in the domain of skin lesion diagnosis are naturally multiclass with a single-label outcome. This is because a skin lesion can seldom be of two disease types concomitantly, except when dealing with different semantics such as a skin lesion that is considered both dermatofibroma and benign. Most of the skin lesion classification methods reported in the literature are mostly limited to binary classes either between melanoma and melanocytic nevi or between broader malignant and benign lesions. However, in recent times, some attempts have been made to solve multiclass skin lesion classifications [19; 39; 40; 41; 42]. Literature has recorded the use of multiple deep learning methods in an ensemble framework to mimic how a medical practitioner leverages a consensus opinion through the pearls of wisdom of other practitioners to corroborate the outcome of a medical diagnosis [43; 44; 45; 46]. Ensemble learning belongs to the state-of-the-art approach that combines the predictions of manifold base learners into a strong algorithm to increase the overall classification performance and decrease the peril of obtaining a local minimum [47]. It addresses the inherent challenges such as the high variance of being sensitive to training data and bias frequently associated with single learners in a deep learning framework. Several previous works have achieved improved results, reduced classification errors and obtained better generalization in skin lesion classification with ensemble learning [29; 48; 49; 50; 51]. Ensemble learning can be achieved by aggregating the prediction from each learner to build a metamodel that produces the final prediction. It includes boosting (AdaBoost, GradientBoost, and eXtreme GradientBoost), bagging (bootstrap aggregation like the random forest), voting (simple majority, averaged majority, or weighted majority), and stacking (stacked generalization for layer-2 meta-model), each with its intrinsic merits and demerits. The four approaches can use heterogeneous algorithms but bagging and boosting are typically used on multiple subsets of training data on homogenous learners, whereas voting and stacking mostly use heterogeneous learners. The aggregation of multiple predictions in ensemble learning can be done by a voting mechanism either by computing maximum voting, average voting, or weighted average voting of all the predicted classes. The authors in [39] proposed an ensemble learning strategy termed the global-part CNN model with data-transformed ensemble learning (GP-CNN-DTEL). It involves training a global CNN (G-CNN) with downscaled dermoscopic images to produce a classification activation map (CAM) and the training of a network called part CNN (P-CNN) with the resulting CAM. In [51], an efficient lightweight melanoma classification network based on MobileNet and DenseNetI21 was proposed to improve the ability of feature discrimination, and recognition accuracy of lightweight networks while utilizing a small number of model parameters. The study in [52] proposed a multi-scale multi-CNN (MSM-CNN) fusion based on a three-level ensemble strategy. This was achieved by fusing the results of three fine-tuned networks of EfficientNetB0, EfficientNetB1, and SeReNeXt-50 with cropped images at six scales. The authors asserted that image cropping is a better strategy when compared to image resizing for performing skin lesion classification based on transfer learning. The study in [53] fused the outputs of the classification layers of four different DNN architectures of GoogLeNet, AlexNet, residual network (ResNet), and visual geometry group network (VGGNet) into one output. The final classification was achieved based on the weighted outputs of the CNN members. In [54], an ensemble learning encompassing Inception-v4, ResNet-152, and DenseNet-161 was used for malignant classification in a manner reminiscent of mimicking the real-world approach.