# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 4 Results
### Presentation of Results
Also, Figure 7 depicts the average prediction errors for the examined CNN architectures in RGB and Grayscale mode, showing the outperformance of the best CNN architecture with dropout=0.7. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Dense Nodes = 16**} & \multicolumn{8}{c}{**Dense Nodes = 32**} & \multicolumn{8}{c}{**Dense Nodes = 64**} \\ \hline
**Batch size** & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss \\
**= 8** & Val & Val & Test & Test & Val & Val & Test & Test & Val & Val & Test & Test \\ Run 1 & 95.31 & 0.19 & 91.1 & 0.18 & 85.94 & 0.39 & 91.1 & 0.23 & 93.75 & 0.21 & 89.28 & 0.37 \\ Run 2 & 95.31 & 0.23 & 91.1 & 0.22 & 92.19 & 0.18 & 85.71 & 0.25 & 95.31 & 0.35 & 94.64 & 0.26 \\ Run 3 & 85.94 & 0.29 & 87.5 & 0.32 & 92.19 & 0.25 & 87.50 & 0.27 & 92.18 & 0.237 & 92.86 & 0.18 \\ Run 4 & 95.31 & 0.17 & 91.1 & 0.23 & 90.62 & 0.23 & 91.10 & 0.27 & 93.75 & 0.238 & 92.86 & 0.19 \\ Run 5 & 87.5 & 0.27 & 92.86 & 0.16 & 90.62 & 0.22 & 94.64 & 0.21 & 87.5 & 0.29 & 92.85 & 0.24 \\ Average & **91.87** & **0.23** & **90.73** & **0.22** & **90.31** & **0.25** & **90.01** & **0.25** & **92.50** & **0.27** & **92.50** & **0.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CNN model (epochs = 200, pixel = 256 \(\times\) 256, conv (8->16->32), dropout = 0.7) runs for different dense nodes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Batch Size = 8**} & \multicolumn{8}{c}{**Batch Size = 16**} & \multicolumn{8}{c}{**Batch Size = 32**} \\ \hline  & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss \\  & Val & Val & Test & Test & Val & Val & Test & Test & Val & Val & Test & Test \\ Run 1 & 93.75 & 0.21 & 89.28 & 0.37 & 93.75 & 0.15 & 83.33 & 0.37 & 89.06 & 0.42 & 93.75 & 0.207 \\ Run 2 & 95.31 & 0.35 & 94.64 & 0.26 & 90.62 & 0.29 & 89.58 & 0.23 & 95.31 & 0.16 & 87.5 & 0.33 \\ Run 3 & 92.18 & 0.23 & 92.86 & 0.18 & 95.31 & 0.16 & 89.75 & 0.22 & 90.62 & 0.22 & 93.75 & 0.18 \\ Run 4 & 93.75 & 0.24 & 92.85 & 0.19 & 89.91 & 0.29 & 89.58 & 0.22 & 95.31 & 0.15 & 87.5 & 0.314 \\ Run 5 & 87.5 & 0.29 & 92.85 & 0.24 & 90.62 & 0.23 & 87.51 & 0.26 & 92.18 & 0.24 & 93.75 & 0.239 \\ Run 6 & 89.06 & 0.3 & 89.28 & 0.22 & 89.1 & 0.29 & 97.92 & 0.14 & 82.81 & 0.35 & 90.62 & 0.227 \\ Run 7 & 90.62 & 0.287 & 94.64 & 0.21 & 84.38 & 0.32 & 95.83 & 0.2 & 98.43 & 0.16 & 93.75 & 0.21 \\ Run 8 & 92.18 & 0.22 & 87.5 & 0.25 & 92.18 & 0.23 & 93.75 & 0.19 & 85.93 & 0.28 & 87.5 & 0.232 \\ Run 9 & 84.37 & 0.33 & 96.42 & 0.16 & 85.7 & 0.32 & 95.83 & 0.24 & 90.62 & 0.21 & 93.75 & 0.19 \\ Run 10 & 93.75 & 0.176 & 94.64 & 0.154 & 89.06 & 0.37 & 87.5 & 0.26 & 85.93 & 0.39 & 87.5 & 0.32 \\ Average & **91.25** & **0.263** & **92.49** & **0.224** & 89.98 & 0.27 & 91.46 & 0.23 & 90.62 & 0.26 & 90.94 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CNN model 2 (epochs = 200, dropout = 0.7, pixel = \(256\times 256\)) runs for different batch sizes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Dropout = 0.2**} & \multicolumn{8}{c}{**Dropout = 0.4**} & \multicolumn{8}{c}{**Dropout = 0.9**} \\ \hline
**Batch Size** & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss & Acc. & Loss & Acc & Loss \\
**= 8** & Val & Val & Test & Test & Val & Val & Test & Test & Val & Val & Test & Test \\ Run 1 & 85.94 & 0.22 & 91.1 & 0.27 & 93.75 & 0.22 & 93.75 & 0.23 & 95.31 & 0.31 & 85.71 & 0.43 \\ Run 2 & 79.69 & 0.65 & 75 & 1.1 & 87.5 & 0.42 & 85.71 & 0.32 & 82.81 & 0.37 & 82.14 & 0.38 \\ Run 3 & 87.5 & 0.3 & 98.21 & 0.1 & 8