## 4 Experimental Results  
In order to train and evaluate the networks, we split the image data into training, validation, and test sets. The training set consists of the 188 images from the Federico II Hospital plus a set of images obtained by applying a simple augmentation technique. This allows for augmenting the number of the available training samples. In particular, the data augmentation has been achieved by flipping the images vertically, horizontally, and in both ways. The final cardinality of the augmented training set is 756 images. Each validation and test set includes 100 images from the ORCA data set. It is worth noticing that validation and test set are completely disjoint sets. In such a way, we tested the capability of the networks in generalizing the segmentation problem. As stated above, we trained four different models:  
* SegNet. * U-Net. * U-Net with VGG16 encoder. * U-Net with ResNet50 encoder. The source code of the used SegNet network is available at [[https://github.com/apennisi/segnet](https://github.com/apennisi/segnet)]([https://github.com/apennisi/segnet](https://github.com/apennisi/segnet)), while the source code for U-Net and its modifications is available at [[https://github.com/[PERSON]/Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images](https://github.com/[PERSON]/Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images)]([https://github.com/[PERSON]/Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images](https://github.com/[PERSON]/Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images)). The four networks have been trained by using the Adam optimizer, with a learning rate of \(1e-4\), without adopting any decay technique. The input image size has been set to 512 \(\times\) 512 pixels. For computing the loss, we used the Cross-Entropy function [41], which is a loss function widely used in deep learning. Cross entropy helps in speeding up the training for neural networks in comparison to the other losses [42]. The cross entropy for multi-class error computation is defined as follows:  
\[s=-\sum_{c=1}^{M}y_{o,c}\log(p_{o,c}), \tag{1}\]  
where \(c\) is the class id, \(o\) is the observation id, and \(p\) is the probability. Such a definition may also be called categorical cross entropy. The training has been manually stopped after 60 epochs for all the considered network architectures because we noticed a trend of all the network in overfitting the training set. We performed three kinds of experiments:  
1. Using RGB images as input:
2. Taking into account the HSV color representation and concatenating the Hue channel with the Red channel from the RGB space;
3. Using the Red and Value channels. An example of the different color models mentioned above is shown in Figure 7. The original RGB image is decomposed into single channels using the RGB and HSV color models. Then, some channels have been selected as input for the deep network. In particular, we used H + R and R + V.  
The idea of using a multi-spectral input derives from the application of deep learning techniques in the area of precision farming, where multi-spectral inputs have denoted good performance on SSNs for the segmentation of crop and weed plants in images acquired from farming robots [43]. Figure 7: An example of the input images used for training the networks. First row: Original input and corresponding annotated mask. Second row: Red, Green, and Blue channels from the original RGB image. Third row: Hue, Saturation, and Value channels from the transformation of the original RGB image into Hue, Saturation, and Value (HSV). The prediction masks produced by the deep networks have been compared with the ground-truth annotation masks in order to measure the capability of our pipeline in generating accurate results.