# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 4 Results
### Comparison with Benchmark CNNs  
In what follows, an extensive comparative analysis between the state-of-the-art CNNs such as VGG16 [33], ResNet50 [34], Mobile Net [92] and Densenet [35] and our best model was performed. The following well-known CNN architectures were used: (i) ResNet50, is a 50-weight layer deep version of ResNet (Residual neural Network), with 152 layers, based on \"network-in-network\" micro-architectures [94]. ResNet has less parameters than the VGG network, demonstrating that extremely deep networks can be trained using standard SGD (Stochastic gradient descent) and a reasonable initialization function through the use of residual modules. (ii) VGG16 [33], is an extended \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grayscale** & **Precision** & **Recall** & **F1-score** & **Sensitivity** & **Specificity** \\ \hline Malignant & 0.82 & 0.97 & 0.89 & 0.82 & 0.96 \\ Benign & 0.96 & 0.76 & 0.85 & 0.96 & 0.82 \\
**Confusion matrix** & **Malignant** & **Benign** & & & \\ Malignant & 32 & 1 & & & \\ Benign & 7 & 22 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics and confusion matrix for grayscale images. Figure 6: Precision curves for best CNN in grayscale mode: (**a**) accuracy and (**b**) loss. Figure 7: Average prediction errors for the best CNN architecture. version of VGG as it contains 16 weight layers within the architecture. VGGs are usually constructed by using \(3\times 3\) convolutional layers which are stacked on top of each other. DenseNET is an extension of ResNet [95]. Based on recent research, it has been proven that using Neural Networks which have smaller connections between layers, mainly in the case where the dataset is relatively small, give better classification performance. Based on this result, DenseNet was created. It follows the feed-forward technique and each layer has as the input all previous output feature maps [35]. MobileNet convolutes each channel separately instead of combining and flattening them all, with the use of depthwise separable convolutions [92]. Its architecture combines convolutional layers, depthwise and pointwise layers to a total number of 30. These popular CNNs were used for transfer learning and were used with weights that were trained on the ImageNet [93] dataset. Tables 11-13 gather the results of these benchmark CNN models compared with our best CNN configuration. In this research work, after an extensive exploration with the provided architectures of popular CNNs, the following parameters for the well-known CNNs were defined. * Best CNN: batch-size = 8, dropout = 0.7, nodes (3 conv layers) = 8,16,32, dense nodes: 64, epochs:200, pixel = (256,256,3)
* VGG16: pixel size (200 \(\times\) 200), batch size = 32, dropout = 0.2, dense nodes 2 \(\times\) 512, epochs = 200
* ResNet50: pixel size (224 \(\times\) 224), batch size = 32, dropout = 0.5, dense nodes 32 \(\times\) 32, epochs = 200
* MobileNet: pixel size (250 \(\times\) 250), batch size = 8, dropout = 0.5, global average pooling, dense = 1500 \(\times\) 1500, epochs = 200
* DenseNet: pixel size (200X200), batch size = 8, dropout = 0.9, global average pooling, dense nodes = (128 \(\times\) 128), epochs = 200. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best CNN** & **ResNet50** & **VGG16** & **MobileNet** & **DenseNet** \\ \hline Accuracy & 92.50 & 91.87 & 83.75 & 85.36 & 95 \\ Precision & 0.93 & 0.84 & 0.85 & 0.78 & 0.92 \\ Recall & 0.94 & 1 & 0.9 & 0.99 & 0.99 \\ F1-score & 0.94 & 0.91 & 0.86 & 0.87 & 0.95 \\ Sensitivity & 0.94 & 1 & 0.89 & 0.99 & 0.99 \\ Specificity & 0.92 & 0.78 & 0.75 & 0.66 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Malignant disease class performance comparison of different methods.