# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 5 Evaluation and Results  
In this section, we will discuss the experimental results achieved for aneurysm detection using a classification model. The classification model maps predicted classes with labeled instances, and recognition labels are used to distinguish between the actual class and predicted class. The experimental results for aneurysm detection are based on a dataset that includes medical images of the brain. The images are labeled as either containing an aneurysm or not. The classification model is trained using a subset of the dataset, and the performance of the trained model is evaluated using another subset of the dataset. To evaluate the performance of the model, several metrics are used, including accuracy, sensitivity, specificity, precision, and F1 score. Accuracy is the percentage of correctly classified instances, sensitivity is the percentage of true positives (aneuvysms correctly identified), specificity is the percentage of true negatives (non-aneurysms correctly identified), precision is the percentage of true positives out of all predicted positives, and F1 score is the harmonic mean of precision and recall. All experiments were executed on a 13 th Gen Intel(R) Core(TM) i5-13400F with a clock speed of 2.50 GHz and 32.0 GB RAM. The achieved results were obtained using Nvidia CUDA libraries, and the experiments were conducted on hardware featuring an Nvidia graphics card (NVIDIA GeForce RTX 3060 graphics card), an optimal choice for deep learning models. This graphics card is equipped with advanced hardware support specifically designed for deep learning computations. The outcomes of the classification model are illustrated through a confusion matrix, detailing the counts of true positives, false positives, true negatives, and false negatives. True positives represent correctly identified aneurysms, false positives denote non-aneurysms incorrectly identified as aneurysms, true negatives indicate correctly identified non-aneurysms, and false negatives signify aneurysms incorrectly identified as  
Figure 8: The proposed 3 DCNN-ConvLSTM architecture. non-aneurysms. In a classification model, the mapping of predicted classes with labeled instances is essential. Recognition labels, assigned to classification results generated by the model, serve to distinguish between the actual class and the predicted class. This process yields four possible outcomes:  
* True Positive (_TP_)--The instance is positive and correctly classified as positive. * True Negative (_TN_)--The instance is negative and correctly classified as negative. * False Positive (_FP_)--The instance is negative but incorrectly classified as positive. * False Negative (_FN_)--The instance is positive but incorrectly classified as negative. True positives (_TP_) denote cases where the model accurately predicted the presence of an aneurysm in a patient. False positives (_FP_) represent cases where the model predicted the presence of an aneurysm, but in reality, the patient did not have one. False negatives (_FN_) indicate cases where the model predicted the absence of an aneurysm, but the patient did indeed have one. True negatives (_TN_) are instances where the model correctly predicted the absence of an aneurysm in a patient. The confusion matrix serves as a foundation for calculating performance metrics for the model, including accuracy, precision, recall, and the F1 score. In this binary classification scenario, these parameters can be derived directly from the confusion matrix. Various metrics are available for evaluation, with specific emphasis on Specificity (true negative rate (_TNR_), Sensitivity (true positive rate (_TPR_)/Recall (_R_)), positive predictive value (_PPV_/Precision (_P_)), and negative predictive value (_NPV_).