## 3 Results
### First Scenario  
The experimental analysis was conducted by passing the extracted features from each DL model to SVM for classification. The performance metrics were calculated for each DL model in every dataset. For each dataset and deep learning model, data were divided into 80% training and 20% testing for each class. Three datasets have been used in this study to compare the performance of the six deep learning models in the feature extraction phase. The six deep learning models that were implemented are:1. ResNet-18
2. AlexNet
3. GoogleNet
4. VGG-16
5. MobileNetV2
6. DenseNet-201  
A. **Dataset 1**  
A comparative study has been conducted on Dataset 1 to evaluate the performance of the image enhancement techniques on the DL models. Dataset 1 featured multiple scans for patients from the following classes:  
7. Normal: 99 patients
8. Benign: 62 patients
9. Malignant: 39 patients  
The performance of six deep learning models was compared twice for Dataset 1. In the first trial, after the augmentation stage, the images were passed to the colour feature map layer without using the image enhancement techniques mentioned in the pre-processing stage. In the second trial, DBT slices were augmented, then enhanced and a colour feature map was applied before being passed to the DL models. This was performed to check whether the image enhancement techniques proposed in the pre-processing stages improved the DBT classification performance or otherwise. Figure 5 demonstrates the confusion matrices of the six deep learning models before applying the image and contrast enhancement techniques. The comparison between the DL models in terms of accuracy and run-time before pre-processing stage is presented in Table 4.  
Figure 5: Confusion matrices on Dataset 1 before pre-processing for the DL models (**a**) ResNet-18, (**b**) AlexNet, (**c**) GoogleNet, (**d**) VGG-16, (**e**) MobileNetV2, (**f**) DenseNet-201. Table 4 and Figure 5 demonstrate that AlexNet obtained the highest accuracy and runtime, with an accuracy of 56.52% and a runtime of 706 s. With runtimes of 737, 4301, and 842 s, ResNet-18, VGG-16, and MobileNetV2 achieved accuracies of 48.05%, 48.69%, and 48.67%, respectively. Finally, GoogleNet and DenseNet-201 yielded the lowest accuracies of 44.82% and 45.95%, respectively, with runtimes of 916 and 4123 s.  
Figure 6 features the confusion matrices of the six deep learning models after implementing the pre-processing phase before the feature extraction phase. The comparison between the DL models in terms of accuracy and run-time after pre-processing is presented in Table S5. Based on Table 4 and Table S5, it is concluded that the accuracy has improved in all DL models after applying the pre-processing stage. As for the run-time, AlexNet, ResNet-18, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Deep Learning Model** & **Accuracy** & **Run-Time (s)** \\ \hline
**ResNet-18** & 48.05\% & 737 \\
**AlexNet** & 56.52\% & 706 \\
**GoogleNet** & 44.82\% & 916 \\
**VGG-16** & 48.69\% & 4301 \\
**MobileNetV2** & 48.67\% & 842 \\
**DenseNet-201** & 45.95\% & 4123 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the six DL models on Dataset 1 before pre-processing. Figure 6: Confusion matrices on Dataset 1 after pre-processing for the DL models (**a**) ResNet-18, (**b**) AlexNet, (**c**) GoogleNet, (**d**) VGG-16, (**e**) MobileNetV2, (**f**) DenseNet-201. and MobileNetV2 needed less time to extract features from the enhanced images, thus reducing the run-time. Despite the insignificant increase in the run-time for GoogleNet, DenseNet-201, and VGG-16 there was an obvious increase in the accuracy of classification. In terms of accuracy, after applying the image enhancement techniques, MobileNetV2 and GoogleNet managed to achieve the highest improvement of 2.86% and 2.78%, respectively. ResNet-18 and GoogleNet achieved an 0.6% and 2.78% accuracy improvement. Finally, AlexNet and DenseNet-201 recorded an improvement of 0.34% and 0.16%, respectively.