# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 6 Discussion  
The integration of 3D convolutional neural networks (3 DCNNs) with Convolutional Long Short-Term Memory networks (ConvLSTM) in our approach has proven to be a pivotal element. This combination allows for a comprehensive analysis of the volumetric data, capturing both spatial and temporal features. This is particularly crucial in the context of brain aneurysm detection, where the dynamic nature of blood flow and the evolution of vascular structures over time play a significant role. One of the key advantages of our approach is the model's enhanced sensitivity to temporal changes associated with aneurysms. The ConvLSTM enables the network to capture the temporal context, allowing it to recognize subtle alterations in the vascular structures. This is especially relevant for aneurysms that may exhibit dynamic morphological changes over consecutive frames in medical imaging sequences.  
The spatiotemporal feature learning capability of our model contributes to an improved accuracy in aneurysm detection. By considering both spatial and temporal depen  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Observed/Predicted** & **Aneurysms** & **Non-Aneurysms** \\ \hline Aneurysms & 0.9919 & 0.063 \\ Non-aneurysms & 0.0081 & 0.937 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The confusion matrix of proposed 3 DCNN-ConvLSTM model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & **Proposed 2D-CNN** & **Proposed 3 DCNN-ConvLSTM** & **[PERSON] [18]** & **[PERSON] [16]** & **[PERSON] [8]** & **[PERSON] [19]** \\
2D-CNN & **3 DCNN-ConvLSTM** & **1D-CNN** & **3D-CNN** & **3D-CNN** & **CNN/MIP** \\ \hline Accuracy & 0.964 & 0.978 & 0.958 & n/a & 0.953 & 0.941 \\ F1-score & 0.965 & 0.982 & 0.957 & n/a & 0.951 & 0.939 \\ Specificity & 0.991 & 0.997 & n/a & 0.823 & n/a & n/a \\ Recall & 0.940 & 0.963 & 0.952 & 0.866 & 0.943 & 0.937 \\ Precision & 0.982 & 0.994 & 0.962 & n/a & 0.958 & 0.943 \\ NPV & 0.937 & 0.965 & n/a & n/a & n/a & n/a \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison of results of the proposed methods results with results of other authors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Observed/Predicted** & **Aneurysms** & **Non-Aneurysms** \\ \hline Aneurysms & 0.9843 & 0.0721 \\ Non-aneurysms & 0.0157 & 0.9279 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The confusion matrix of proposed 2D convolutional model (2 DCNN).  
dencies, the network is better equipped to distinguish between normal vascular variations and potential aneurys anomalies. This leads to a reduction in false positives, enhancing the overall reliability of the detection system. Our novel approach demonstrates promising adaptability to patient-specific variations in aneurysm presentation. The ConvLSTM's ability to capture long-term dependencies enables the model to learn and adapt to the unique characteristics of aneurysms in individual patients. This contributes to a more personalized and robust detection mechanism across diverse clinical scenarios.  
In cases where medical imaging provides four-dimensional (4D) data, our approach effectively utilizes this additional temporal dimension. The 3 DCNN + ConvLSTM architecture proves advantageous in extracting meaningful insights from the time-dependent evolution of vascular structures, further enhancing the accuracy of aneurysm detection in dynamic imaging sequences.  
Despite the promising results, challenges remain, including the need for large, diverse datasets and addressing potential biases in the training data. Ethical considerations, such as patient privacy and model interpretability, need careful attention as the technology advances. Striking a balance between innovation and ethical responsibility is crucial for the responsible deployment of such models in clinical practice.