# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 7 Conclusions  
In this article, we focused on the detection of cerebral aneurysms. We used deep neural networks for this purpose. We compared two types of DNNs, namely 2 DCNN and 3 DCNN-ConvLSTM. In both cases, we had comparable results in terms of accuracy. The convolutional 2D model slightly improved the scoring on the test set. The 2 DCNN model was computationally more demanding but reached its optimum after approximately 60 epochs. Afterwards, it showed signs of overfitting. The 3 DCNN-ConvLSTM model reached its limits around 100 epochs. In our work, we would like to develop an annotation tool that would use the proposed model to quickly search regions with potential aneurysms. These detections would then be confirmed or rejected by a specialist radiologist. The new data would refine the existing model. Some metrics were not reported by foreign authors; for this reason, we made the results somewhat redundant to make them easier to compare. Aneurysm classification is an important task in medical imaging and is crucial for the diagnosis and treatment of brain aneurysms. The traditional method of aneurysm classification relies on manual inspection of medical images by a trained radiologist. However, with the advancements in deep learning and computer vision, automated aneurysm classification using deep neural networks has gained a lot of attention in recent years. In this discussion, we will explore the current state of aneurysm classification using deep neural networks and suggest some future directions for research in this field. Deep neural networks have shown promising results in various medical imaging tasks, including aneurysm classification. These networks can automatically extract relevant features from medical images and classify them into different categories. Convolutional neural networks (CNNs) are a popular type of deep neural network that has been used for aneurysm classification. CNNs have been used to extract features from medical images and to classify them into different categories such as aneurysm size, shape, and location. One of the challenges in aneurysm classification using deep neural networks is the lack of annotated medical images. Annotated medical images are necessary to train deep neural networks. However, the availability of annotated medical images is limited due to various reasons such as privacy concerns and the high cost of annotation. Therefore, researchers have used transfer learning to overcome this challenge. Transfer learning involves training deep neural networks on a large dataset of non-medical images and then fine-tuning the network on a smaller dataset of medical images. This approach has shown promising results in aneurysm classification. Another challenge in aneurysm classification using deep neural networks is the class imbalance problem. In medical imaging, the number of normal cases is usually much larger than the number of abnormal cases. This class imbalance problem can result in a biased model that performs well on normal cases but poorly on abnormal cases. Researchers have used different techniques to address the class imbalance problem, such as oversampling the minority class, undersampling the majority class, and generating synthetic data. The proposed approach for brain aneurysm detection using deep neural networks holds significant applicability in the field of medical imaging and healthcare. The method leverages deep neural networks to analyze medical imaging data, indicating its applicability in the advancement of diagnostic capabilities within medical imaging technologies. Deep neural networks have shown prowess in learning intricate patterns, enabling the model to accurately detect and identify brain aneurysms in medical images. This contributes to enhanced diagnostic precision. Early detection of brain aneurysms is crucial for timely intervention and treatment planning. This method provides a means to identify potential issues in their nascent stages, facilitating proactive medical measures.