## 3 Results
### Classification Results  
#### 3.2.1 Database  
For the classification step, the ISIC-2019 database has been used [44; 45], which contains 25,331 dermoscopic images. This database consists of the following lesions: actinic keratosis (867 images), Basal Cell Carcinoma (3323 images), benign keratosis (2624 images), dermatofibroma (239 images), melanoma (4522 images), nevus (12,875 images), squamous cell carcinoma (628 images), and vascular lesion (253 images). An example of these dermoscopic images is shown in Figure 5.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Methodology** & **DICE** & **JACC** & **Se** & **Sp** & **ACC** \\ \hline SegNet & 0.8548 & 0.7730 & 0.8533 & 0.9632 & 0.9357 \\ Energy minimization & 0.5937 & 0.4927 & 0.6020 & 0.9131 & 0.8647 \\ Feature images from VGG16 & 0.5853 & 0.4700 & 0.8627 & 0.8690 & 0.8170 \\ \hline \end{tabular}
\end{table}
Table 1: Segmentation results of the tested methods over the Test set (600 images). Numbers represent the average values of the different segmentation performance parameters calculated over the 600 test images.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Ref.** & **Methodology** & **DICE** & **JACC** & **Se** & **Sp** & **ACC** \\ \hline
[41] & FCDN- First Place at Challenge & & & & & \\  & ISBI-2017 & & & & & \\
[27] & Separable-UNet model with & & & & & \\  & stochastic weight averaging scheme & & & & & \\
[42] & LinkNet152 model & & & & & \\
[43] & GAN-based model & 0.9063 & 0.8198 & 0.8781 & 0.9992 & 0.9761 \\
[26] & Deep class-specific learning & 0.8566 & 0.7773 & 0.8620 & 0.9671 & 0.9408 \\  & SegNet & 0.8548 & 0.7730 & 0.8533 & 0.9632 & 0.9357 \\ \hline \end{tabular}
\end{table}
Table 2: Segmentation results of benchmark methods over the Test set (600 images). The results obtained with the SegNet neural network have also been included to facilitate the comparison.  
In order to train the convolutional neural networks used to classify the lesions, the database was balanced by a data augmentation process. Data augmentation was carried out by mirror operations and rotations of 36 degrees over each image. The test images were not modified.  
As mentioned above, two different experiments were carried out, hence, the database was balanced in two different ways.  
For the classification of BCC vs. Nevus, after data augmentation, the following number of images were obtained: 8982 images for training and 1287 for validation, for each class. In this sense, for all the classes, except nevus, the number of images was artificially augmented by the data augmentation process described above. For the nevus class, a downsampling process has been carried out, randomly selecting 10,269 images in total (8982 training + 1287 validation) out of the 12,875 nevus images that the ISIC provides.  
For BCC vs. All lesions, the balanced dataset is constituted as follows: 15,419 training images and 2199 validation images, for each of the two classes (BCC and the rest of the types). In this case, data augmentation was applied for the BCC class, increasing the number of nevus images up to 17,618 in total (training and validation set). For each class belonging to the remaining seven classes, the number of images was fixed to 2517 images in total for each class.