# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 4 Results
### Receiver Operating Characteristic (ROC)
\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 DSTL)**} & **AlexNet+** & 61.50\% & 92.11\% & 95.24\% & 88.24\% & 96.92\% \\  & **ShuffleNet+** & 80.88\% & 96.49\% & 96.82\% & 96.07\% & 99.44\% \\  & **MobileNet-v2+** & 83.46\% & 98.25\% & 98.41\% & 98.03\% & 99.53\% \\
**MIAS** & **GoogLeNet+** & **86.30\%** & 96.49\% & 98.41\% & 94.11\% & 99.69\% \\
**(With DSTL)** & **ResNet-50+** & **82.43\%** & 95.61\% & 96.82 \% & 94.11\% & 99.25\% \\  & **ResNet-101+** & **87.08\%** & 97.37\% & 98.41\% & 96.07\% & 99.60\% \\  & **VGG-16+** & **77.00\%** & 93.86\% & 96.82\% & 90.19\% & 99.04\% \\  & **VGG-19+** & **76.23\%** & **93.86\%** & **95.24\%** & **92.16\%** & **99.28\%** \\ \hline \multirow{8}{*}{BCDR (With SSTL)} & AlexNet & 65.20\% & 73.38\% & 77.27\% & 69.46\% & 80.64\% \\  & ShuffleNet & 70.86\% & 81.75\% & 75.75\% & 87.78\% & 88.57\% \\  & MobileNet-v2 & 69.60\% & 82.13\% & 80.30\% & 83.96\% & 88.71\% \\  & GoogleNet & 74.00\% & 83.65\% & 83.33\% & 83.96\% & 91.59\% \\  & ResNet-50 & 73.38\% & 77.95\% & 70.45\% & 85.49\% & 85.94\% \\  & ResNet-101 & 77.78\% & 81.37\% & 77.27\% & 85.49\% & 89.90\% \\  & VGG-16 & 73.17\% & 79.09\% & 77.27\% & 80.92\% & 89.14\% \\  & VGG-19 & 81.97\% & 84.41\% & 82.57\% & 86.25\% & 91.99\% \\ \hline \multirow{8}{*}{**BCDR (With DSTL)**} & **AlexNet+** & **77.36\%** & **82.13\%** & **83.33\%** & **80.91\%** & **93.20\%** \\  & **ShuffleNet+** & **81.97\%** & **87.83\%** & **86.36\%** & **89.31\%** & **94.74\%** \\ \cline{1-1}  & **MobileNet-v2+** & **82.39\%** & **86.31\%** & **80.30\%** & **92.36\%** & **91.51\%** \\ \cline{1-1}  & **GoogLeNet+** & **87.84\%** & **88.21\%** & **86.36\%** & **90.07\%** & **95.65\%** \\ \cline{1-1}  & **ResNet-50+** & **83.65\%** & **87.07\%** & **84.09\%** & **90.07\%** & **93.92\%** \\ \cline{1-1}  & **ResNet-101+** & **82.81\%** & **87.07\%** & **81.06\%** & **93.13\%** & **94.29\%** \\ \cline{1-1}  & **VGG-16+** & **81.76\%** & **87.97\%** & **90.91\%** & **87.02\%** & **94.52\%** \\ \cline{1-1}  & **VGG-19+** & **86.79\%** & **89.11\%** & **89.39\%** & **90.84\%** & **94.57\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset \& Technique** & **Model** & **Training-Time** & **Epoch** & **Iteration** \\ \hline \multirow{8}{*}{**MIAS (With SSTL)**} & AlexNet & 04 min 22 s & 48 & 1824 \\  & ShuffleNet & 17 min 51 s & 38 & 1444 \\  & MobileNet-v2 & 12 min 17 s & 20 & 760 \\  & GoogleNet & 08 min 50 s & 29 & 1102 \\  & ResNet-50 & 11 min 30 s & 18 & 684 \\  & ResNet-101 & 37 min 17 s & 25 & 950 \\  & VGG-16 & 09 min 55 s & 21 & 798 \\  & VGG-19 & 25 min 13 s & 46 & 1748 \\ \hline \multirow{8}{*}{**MIAS (With DSTL)**} & AlexNet+** & **02 min 43 s** & **21** & **798** \\  & ShuffleNet+** & **11 min 49 s** & **28** & **1064** \\ \cline{1-1}  & **MobileNet-v2+** & **11 min 35 s** & **19** & **722** \\ \cline{1-1}  & **GoogLeNet+** & **08 min 08 s** & **25** & **950** \\ \cline{1-1}  & ResNet-50+** & 09 min 54 s & **16** & **608** \\ \cline{1-1}  & ResNet-101+** & 31 min 23 s & **21** & **798** \\ \cline{1-1}  & VGG-16+** & 08 min 22 s & **18** & **684** \\ \cline{1-1}  & VGG-19+** & 08 min 48 s & **15** & **570** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance analysis of different pre-trained networks with SSTL and DSTL on MIAS and BCDR datasets.