# Automatic Tumor Identification from Scans of Histopathological Tissues
## 4 Experiments and Results  
### Experimental Setup  
As we stated previously, the U-Net type architecture is currently one of the most popular templates to search for a suitable architecture. It allows you to easily check the signal information of different levels individually and by combining high and low frequency signals in separate levels; in our work, we called it the M-model, shown in Figure 4.  
By default, U-Net networks form a complete U-shape--they transmit the entire input signal to the output of the model, which is applicable in this work due to efficiency and the type of task itself. Higher resolution output is usually used for image reproduction or segmentation tasks. In this research, classification was performed from low-resolution images, so the high-resolution output will not be superior. The following changes were made to the intermediate modules of the network structure, that we will call the E-Module, shown in Figure 5. Compared to the commonly used standard ResNet module, the E module was more efficient and faster--two 3 \(\times\) 3 convolutions were changed to one compressed 3 \(\times\) 3 convolution and one 1 \(\times\) 1 convolution, with the same number of  
Figure 4: The M-model based on the U-Net network architecture. channels as the number of input layer channels. Furthermore, the typical ReLU (rectified linear unit) activation was changed to PReLU (parametric rectified linear unit) [75]. In this way, the model will be able to apply the best activation for each layer according to the direction of the gradient. Together with this activation function, it was possible to see the behavior of the model by analyzing the values of the activation coefficients. Finally, a Dropout layer was added at the end of the module, which performs a dual regularization: it reduces the signal bandwidth and allows the dropout factor to be increased to 1 at the end of training to discard supposedly unnecessary parts of the model. Additionally, the entire network structure was reworked into a dynamic format. Based on the principle of the EfficientNet architecture [76], the rules for growing the width and length of the network were established and adaptive regularization were applied with Equations (5)-(9) shown below:  
* Number of filters (5): \[\text{Number of filters }=max\bigg{(}16,\ \frac{f\ \cdot\ s\ \cdot\ F}{h\ \cdot 2} \bigg{)}\ \cdot\ g\] (5)
* Number of blocks (6): \[\text{Number of blocks }=max\bigg{(}2,\ \frac{\text{Number of filters }}{64}\bigg{)}\] (6)
* Exclusion factor (7): \[\text{Exclusion factor }=max\bigg{(}0.05,\ \frac{\text{Number of filters }}{m/2}\bigg{)}\] (7)
* L2 regularization (8): \[\text{L2 regularization }=1\times e-5+\frac{f}{8}\times 0.0001\] (8)  
where \(f\) is the base number of filters, \(F\) is the filter multiplier, \(s\) is the filter multiplier, \(h\) is the height of the network in pixels, \(g\) is the number of convolutional groups of the network. The number of blocks indicates how many internal blocks will make up the mesh module after each decrease or increase in height and width of the mesh. Exclusion factor--indicates how many neurons will be turned off in percentage in each block, where m indicates the  
Figure 5: E-module based on ResNet block design. maximum possible number of filters. The L2 regularization [77; 78] specifies the value of the L2 regularization constant for all network convolutions. To understand whether all the changes made to the model gave advantages compared to ResNet50 or DenseNet121 models, the following experiments were performed. According to the already presented formula for changing the structure of the model, three sizes for \(f\) were selected: 8; 16; 32. Sizing allows us to see the areas where the network performs too poorly, overlearns, and where it performs optimally. It is additionally important to find out from which part of the network the best result can be obtained. Therefore, five output layers were added to the already existing network. Accordingly, we noticed a trend while comparing the results of all outputs.