## 3 Results and Discussion
### Discussion  
The proposed classification tool works efficiently either as individual ResNet-50 networks or a double-branch deep learning architecture. There is a balance in the results produced by standalone single-branch ResNets with mostly slight differences. That suggests that both input data representations contribute almost equally to the proper hemorrhage classification. The IVH is the best-classified subtype featuring accuracy and F1 scores exceeding 95% in both branches. The worst performance can be observed in the EDH (accuracy below 70%, F1 score below 60%). In a double-branch CNN architecture supported by SVM and RF classifiers, IVH and EDH classification performs most and least efficiently, respectively. The random forest has a slight advantage over the support vector machine except for the EDH. However, keep in mind that the EDH produces classification results significantly lower than the other subtypes. The subtype-classification-assessment ranking reflects the order yielded by individual ResNets. Nonetheless, double-branch architecture improves classification in each subtype. The gains in accuracy and F1 score differ between subtypes--from ca. 1-2% in SDH, IPH, IVH, and SAH to over 10% in EDH, where the room for growth is the largest. The consistency of improvement throughout various ICHs leads us to conclude that the employment of an external machine learning classifier taking advantage of the double-source deep features is profitable in ICH detection. Improved performance of the proposed double-branch architecture can be associated with the use of multi-source features for classification. The fusion of features extracted by networks addressing specific image data representations provides a broader range of information. A slice-to-slice neighborhood context appears to be particularly relevant. Considering the entire CT study arranged in \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ICH Subtype** & **Feature-Extraction Branch** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **F1 (\%)** \\ \hline \multirow{2}{*}{Subdural (SDH)} & Branch \#1 & 88.7 & 87.6 & 89.9 & 88.9 \\  & Branch \#2 & 86.9 & 86.3 & 87.6 & 87.2 \\ \hline \multirow{2}{*}{Epidural (EDH)} & Branch \#1 & 58.6 & 56.2 & 61.0 & 57.5 \\  & Branch \#2 & 66.4 & 47.7 & 85.1 & 58.6 \\ \hline \multirow{2}{*}{Intraparenchymal (IPH)} & Branch \#1 & 92.4 & 91.7 & 93.1 & 92.2 \\  & Branch \#2 & 92.1 & 91.7 & 92.5 & 92.0 \\ \hline \multirow{2}{*}{Intraventricular (IVH)} & Branch \#1 & 95.5 & 94.9 & 96.0 & 95.1 \\  & Branch \#2 & 95.7 & 96.0 & 95.4 & 95.6 \\ \hline \multirow{2}{*}{Subarachnoid (SAH)} & Branch \#1 & 88.0 & 87.3 & 88.8 & 87.8 \\  & Branch \#2 & 88.2 & 91.0 & 85.4 & 88.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of evaluation metrics obtained in Experiment #1 for individual ICH subtypes and ResNet models. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ICH subtype** & **Classifier** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **F1 (\%)** \\ \hline \multirow{2}{*}{Subdural (SDH)} & SVM & 87.1 & 83.7 & 90.7 & 87.0 \\  & RF & 89.1 & 86.7 & 91.7 & 89.1 \\ \hline \multirow{2}{*}{Epidural (EDH)} & SVM & 76.9 & 73.4 & 80.1 & 75.3 \\  & RF & 74.3 & 68.6 & 79.9 & 72.7 \\ \hline \multirow{2}{*}{Intraparenchymal (IPH)} & SVM & 91.9 & 93.1 & 90.7 & 91.7 \\  & RF & 93.3 & 93.9 & 92.6 & 93.1 \\ \hline \multirow{2}{*}{Intraventricular (IVH)} & SVM & 96.0 & 97.1 & 94.9 & 95.9 \\  & RF & 96.7 & 96.7 & 96.7 & 96.6 \\ \hline \multirow{2}{*}{Subarachnoid (SAH)} & SVM & 86.8 & 86.1 & 87.6 & 87.6 \\  & RF & 89.7 & 90.0 & 89.4 & 89.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of evaluation metrics obtained in Experiment #2 for individual ICH subtypes and classifiers. the correct order, hemorrhage should be observed in subsequent slices. Thus, such spatial information improves the cognitive capabilities of the automated system.