## 3 Results
### Classification Results
#### 3.2.2 Classification Results of BCC vs. Nevus  
The database consists of 23,780 images in total, after the data augmentation. The training set consists of 17,964 images, the validation set consists of 2574 images and there are 3242 images for the test process. The results are described in Tables 3 and 4.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Se** & **Sp** & **Pre** & **FPR** & **Acc** \\ \hline VGG16 & 0.95 & 0.97 & 0.88 & **0.99** & 0.97 \\ ResNet50 & 0.95 & 0.97 & 0.88 & **0.99** & 0.96 \\ InceptionV3 & 0.95 & 0.97 & 0.87 & 0.96 & 0.94 \\ Ensemble ArgMax & **0.97** & **0.98** & **0.92** & **0.99** & **0.98** \\ Ensemble Mean & **0.97** & 0.97 & 0.90 & **0.99** & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance parameters for the different classifiers without the previous segmentation of the image when classifying BCC vs. Nevus. Se: Sensitivity, Sp: Specificity, Pre: Precision, FPR: False Positive Rate, Acc: Accuracy. The highest values are shown in bold numbers.  
Figure 5: Dermoscopic images from ISIC-2019 database. (**a**) Squamous cell carcinoma; (**b**) Nevus; (**c**) Melanoma; (**d**) Dermofibroma; (**e**) Benign keratosis; (**f**) Basal Cell Carcinoma; (**g**) Actinic keratosis; (**h**) Vascular lesion.  
Transfer learning has been applied to the pre-trained neural network configurations.  
If we denote true positives as TP, true negatives as TN, false positives as FP and false negatives as FN, the classification performance parameters we have used are defined as follows:  
Sensitivity, which represents the proportion of people who test positive among all those who actually have the disease: Se = TP/(TP + FN).  
Specificity, which is the proportion of people who test negative among all those who actually do not have that disease: Sp = TN/(TN + FP).  
Precision, which represents the probability that following a positive test result, that individual will truly have that specific disease: Pre = TP/(TP + FP).  
False positive rate, which is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events: FPR = FP/(FP + TN).  
Accuracy, which represents the proportion of true positive results (both true positive and true negative) in the selected population: Acc = (TN + TP)/(TN + TP + FN + FP).  
As shown in Tables 3 and 4, the best configuration is the ensemble ArgMax, without previous segmentation of the image. For this case, the confusion matrix is shown in Figure 6. The confusion matrix shows the classification performed by the specialists (named _True_), versus the predicted classification performed by the ensemble ArgMax classification tool. The larger the numbers in the diagonal are, the better the classification results are.  
can be observed, the proposed method outperforms the benchmark algorithm in almost all the metrics.  
The reason for these good results may be found in different factors. First of all, the three networks chosen for the ensemble are networks of proven effectiveness (VGG16, ResNet and Inceptionv3 were the winners in the ILSVRC 2014 or ILSVRC 2015 challenges). On the other hand, these networks do not have an excessive number of parameters, which would require a large training database to obtain good classification results. Finally, the ensemble of these three winning networks leads to the obtaining of better results.