# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 4 Experimental Results
### Stage 2 Evaluation

Table 6: _Cont._(99.43%), precision (87.16%), Jaccard index (0.66), and Dice coefficient (0.80), and Matthew correlation coefficient (0.80) in the discrimination of AKIEC. It recorded the most promising result of sensitivity (91.24%), specificity (99.57%), balance accuracy (99.12%), precision (92.54%), and Jaccard index (0.85) with dice-coefficient (0.92) for malignant BCC skin lesions. The two algorithms had the best Matthew correlation coefficient result of 0.91 for the same BCC lesion class classification. Similarly, for the classification of the BKL lesion class, the WMVE algorithm chronicled the best performance based on the sensitivity (82.75%), specificity (99.29%), balance accuracy (98.60%), precision (83.61%), Jaccard index (0.71), dice-coefficient (0.83) and Matthew correlation coefficient (0.82). The WMVE algorithm had the best results for balance accuracy (99.93%), dice coefficient (0.92), and Matthew correlation coefficient (0.92) for the classification of DF skin lesions. It has equally demonstrated its prowess for the classification of malignant MEL by recording the best balance accuracy score of 96.77%, precision score of 84.04%, Jaccard index of 0.67, dice coefficient of 0.80, and Matthew correlation coefficient of 0.79. The two ensemble algorithms had the best dice coefficient result of 0.93 for the classification of the benign NV lesion class. However, the WMVE algorithm recorded the best results of sensitivity (93.18%), specificity (96.88%), balance accuracy (95.74%), precision (93.00%), and Jaccard index (0.87) with Matthew correlation coefficient (0.90) for the classification of the same NV lesion. The classification of malignant SCC has equally shown the strength of both algorithms, with the SMVE algorithm recording the best precision value of 88.39%. They had the best dice coefficient (0.85) and Matthew correlation coefficient (0.85) for the same SCC skin lesion class prediction. But the WMVE algorithm outperformed the rest of the compared algorithms for SCC classification in terms of sensitivity (82.79%) and balance accuracy (99.70%). The SMVE algorithm had the same specificity (100.00%) and precision (100.00%) as the IG-ResNeXt-101 base learner for the classification of VASC lesions. However, the best results for balance accuracy (99.97%), Jaccard index (0.94), dice coefficient (0.97), and Matthew correlation coefficient (0.97) were credited to the SMVE algorithm for the discrimination of VASC lesions. The WMVE algorithm has equally recorded the best balance accuracy of 97.08% for classifying skin lesions categorized as INDB, whose specific benign properties could not be ascertained. The two algorithms have demonstrated equal discriminating capability for INDB lesions with the DenseNet-201 base learner considering the reported Jaccard index (0.94), dice coefficient (0.97), and Matthew correlation coefficient (0.94). Figure 4 shows the confusion matrix of the base learners used to build the ensemble deep learning algorithms for the classification of ten classes of skin lesions. The matrices have revealed that the strength of an ensemble deep learning algorithm is greatly influenced by how well each of its base learners was trained to generalize.