# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 4 Results
### Receiver Operating Characteristic (ROC)  
In this paper, the ROC curve is used to evaluate the performance quality of the pre-trained models and present the Area Under the Curve (AUC) by applying threshold values across the interval [0,1]. For each threshold, two values are calculated, the probability of detection power \(P_{D}\) or the TP ratio and the probability of false alarm or the false positive (FP) ratio. Figures 7-10 show the ROC curve which was used to plot TP versus FP with the threshold as a parameter for AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, ResNet-101, MobileNet-v2, and ShuffleNet on MIAS, and BCDR datasets, respectively. lightweight and non-lightweight models alike and provide faster convergence as shown in Table 9. However, training from random initialization can be analyzed in the future. Figure 8: The ROC curve of various pre-trained models with DSTL for breast cancer classification using the MIAS dataset, where the AUC of each pre-trained model has been improved. Figure 9: The ROC curve of various pre-trained models with SSTL for breast cancer classification using the BCDR dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset \& Model** & **Val. Acc.** & **Testing Acc.** & **Specificity** & **Sensitivity** & **AUC** \\ \hline \multirow{8}{*}{CBIS-DDSM} & AlexNet & 90.18\% & 81.92\% & 86.65\% & 76.11\% & 89.24\% \\  & ShuffleNet & 93.85\% & 89.28\% & 92.51\% & 85.30\% & 95.46\% \\  & MobileNet-v2 & 94.50\% & 92.03\% & 93.39\% & 90.35\% & 96.20\% \\ \cline{1-1}  & GoogleNet & 96.56\% & 93.68\% & 96.26\% & 90.50\% & 97.47\% \\ \cline{1-1}  & ResNet-50 & 96.97\% & 93.20\% & 95.26\% & 90.65\% & 96.59\% \\ \cline{1-1}  & ResNet-101 & 97.09\% & 93.47\% & 95.88\% & 90.50\% & 97.57\% \\ \cline{1-1}  & VGG-16 & 96.20\% & 92.58\% & 93.76\% & 91.18\% & 97.11\% \\ \cline{1-1}  & VGG-19 & 95.12\% & 90.93\% & 93.02\% & 88.36\% & 96.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of various pre-trained models performance using the CBIS-DDSM dataset. Figure 10: The ROC curve of various pre-trained models with DSTL for breast cancer classification using the BCDR dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset \& Technique** & **Model** & **Validation Acc.** & **Testing Acc.** & **Specificity** & **Sensitivity** & **AUC** \\ \hline \multirow{8}{*}{MIAS (With SSTL)} & AlexNet & 56.07\% & 88.60\% & 92.06\% & 84.31\% & 96.14\% \\  & ShuffleNet & 68.73\% & 92.98\% & 93.65\% & 92.15\% & 96.64\% \\ \cline{1-1}  & MobileNet-v2 & 64.60\% & 92.11\% & 93.65\% & 90.19\% & 96.20\% \\ \cline{1-1}  & GoogLeNet & 60.21\% & 88.60\% & 95.24\% & 80.39\% & 98.41\% \\ \cline{1-1}  & ResNet-50 & 72.35\% & 93.86\% & 95.24\% & 92.15\% & 98.41\% \\ \cline{1-1}  & ResNet-101 & 70.80\% & 91.23\% & 95.24\% & 86.27\% & 97.60\% \\ \cline{1-1}  & VGG-16 & 66.15\% & 92.11\% & 96.83\% & 86.27\% & 98.23\% \\ \cline{1-1}  & VGG-19 & 57.88\% & 90.35\% & 90.48\% & 90.19\% & 97.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The comparison of various pre-trained models with SSTL and DSTL on MIAS and BCDR datasets. In Table 9, the number of iterations and epochs is different for each model because we use the validation accuracy monitoring algorithm which helps with reducing the number of iterations and epochs by keeping track of the best validation accuracy and the number of validations, hence, when there has not been any improvement of the validation accuracy (validation lag), an early stop mode of the training process will be triggered. For example, if the validation accuracy is not improving after 10 iterations, the training process will stop automatically.