# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mammograms with 256 \(\times\) 256 resolution**} \\
**Model** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline \multicolumn{5}{c}{**Original images**} \\ ResNet18 & **0.96** & 0.61 & 0.06 & 0.04 & 0.04 \\ ResNet34 & **0.96** & 0.63 & 0.08 & 0.06 & 0.06 \\ ResNet152 & 0.89 & 0.58 & 0.03 & **0.17** & 0.06 \\ EfficientNetB0 & **0.96** & 0.59 & 0.03 & 0.02 & 0.02 \\ MaxViT & 0.95 & **0.66** & **0.09** & 0.15 & **0.11** \\ \hline \multicolumn{5}{c}{**Processed images**} \\ ResNet18 & 0.92 & **0.67** & 0.07 & 0.22 & 0.11 \\ ResNet34 & 0.93 & **0.67** & **0.09** & 0.21 & **0.13** \\ ResNet152 & 0.82 & 0.64 & 0.04 & **0.33** & 0.07 \\ EfficientNetB0 & **0.95** & 0.63 & 0.08 & 0.11 & 0.09 \\ MaxViT & 0.93 & **0.67** & **0.09** & 0.21 & **0.13** \\ \hline \multicolumn{5}{c}{**Processed images + synthetic images**} \\ ResNet18 & 0.92 & 0.83 & 0.26 & 0.58 & 0.36 \\ ResNet34 & 0.94 & 0.82 & 0.34 & 0.56 & 0.42 \\ ResNet152 & 0.84 & 0.8 & 0.15 & **0.63** & 0.24 \\ EfficientNetB0 & **0.95** & 0.8 & **0.39** & 0.52 & **0.45** \\ MaxViT & 0.9 & **0.85** & 0.23 & **0.63** & 0.34 \\ \hline \multicolumn{5}{c}{**Mammograms with 512 \(\times\) 512 resolution**} \\
**Model** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline \multicolumn{5}{c}{**Processed images**} \\ ResNet18 & **0.97** & 0.66 & **0.16** & 0.09 & 0.12 \\ ResNet34 & **0.97** & 0.66 & **0.16** & 0.09 & 0.12 \\ ResNet152 & 0.83 & 0.59 & 0.03 & **0.26** & 0.06 \\ EfficientNetB0 & 0.96 & 0.64 & 0.1 & 0.06 & 0.08 \\ MaxViT & 0.95 & **0.69** & 0.11 & 0.18 & **0.14** \\ \hline \multicolumn{5}{c}{**Processed images**} \\ ResNet18 & 0.93 & 0.72 & 0.1 & 0.26 & 0.15 \\ ResNet34 & 0.94 & 0.71 & **0.13** & 0.24 & **0.16** \\ ResNet152 & 0.83 & 0.69 & 0.05 & 0.4 & 0.09 \\ EfficientNetB0 & **0.95** & 0.69 & 0.12 & 0.18 & 0.14 \\ MaxViT & 0.89 & **0.77** & 0.08 & **0.43** & 0.14 \\ \hline \multicolumn{5}{c}{**Processed images + synthetic images**} \\ ResNet18 & 0.94 & 0.85 & 0.35 & 0.59 & 0.44 \\ ResNet34 & **0.95** & 0.84 & 0.4 & 0.58 & 0.47 \\ ResNet152 & 0.83 & 0.82 & 0.14 & 0.66 & 0.23 \\ EfficientNetB0 & **0.95** & 0.84 & **0.47** & 0.55 & **0.51** \\ MaxViT & 0.89 & **0.88** & 0.22 & **0.72** & 0.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results. The results suggest that while larger and more complex models may not always be necessary, careful pre-processing and data augmentation techniques can significantly improve the performance of even simple models. This highlights the importance of optimizing the pre-processing steps and the potential of synthetically generated data to enhance the performance of models in computer vision tasks. After the deep learning model made a classification decision on an image, two visualization techniques were applied to better understand how the model made its decision. The first visualization technique involved generating class activation maps. This technique was used to identify the regions of the input image that were most important for the model's classification decision. A corresponding layer was applied to the model output to generate the class activation maps. These maps highlighted the specific areas of the image that the model used to make its decision and provided visual evidence of the model's reasoning. The second visualization technique involved generating a centered bounding box around the area of the image that was most important for the classification decision. This technique provided a more intuitive way of understanding the model's decision-making process by highlighting the specific area of the image that was most relevant for the classification. The area with the highest activation value was identified and a centered bounding box was generated around that area. The resulting bounding box was presented in Figure 6, providing a clear visual representation of the area of the image that the model used to make its decision.