# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 1 Introduction
The two radiologists reached slightly better sensitivity of 86% and 88%, while the average value was around 70% for the human. In each case, the AI-assisted analysis became faster. [PERSON] et al. [7] focused on using AI for acute stroke imaging. In [8], the detection of a pulmonary nodule using multilevel 3D CNN is presented. had them categorized according to size and location. They also evaluated the cases of false positives and false negatives. In [13; 14], they compared the work of several teams using different methods and approaches to detect aneurysms in the context of the [PERSON] challenge on 3D data. [PERSON] et al. [15] investigated CNNs on MRI images for unusual aneurysms of different sizes. [PERSON] et al. [16] also used a 3D-CNN-based spatial model. They compared the accuracy of different types of models based on DNN principles. [PERSON] et al. [17] used an automatic diagnosis approach based on the spatial information of fusion features. [PERSON] et al. [18] also performed a similar study, but they proposed a 1D-CNN and compared their results with other authors. Nowadays, artificial intelligence (AI) is increasingly used in the management of patients with cerebrovascular disease, especially in the case of Cerebral Misery Perfusion (CMP stroke). AI assists physicians and healthcare professionals in identifying risk factors and predicting treatment outcomes. In addition, AI also helps in diagnosis, for example, in identifying possible symptoms of stroke and determining their severity. Another improvement that AI brings in the management of CMP patients is aneurysm detection [19]. An aneurysm is a condition where there is a bulge in the wall of a blood vessel that can cause bleeding into the brain. Detection of an aneurysm is very important because early diagnosis can prevent serious consequences and save the patient's life. The use of AI in aneurysm detection allows doctors to identify the symptoms of the condition by analyzing imaging scans such as CT scans or MRIs. The results of the analysis are then processed and interpreted using machine learning algorithms, allowing doctors to provide more accurate and faster diagnoses. The benefit of using AI in the management of CMP patients is primarily to improve diagnosis and prediction of treatment outcomes, leading to better patient health outcomes. It also allows doctors and healthcare professionals to work faster and more efficiently, leading to an improved overall healthcare system [20]. Our work and methods emerged from the data we had available. We focused on processing CTA images captured from a series of angles around the head. We tested different types of deep neural networks to optimize the accuracy of brain aneurysm detection. In the methodology, we describe in detail the form of the data used, aneurysm annotation, and pre-processing. We tried a two-dimensional convolutional neural network (2 DCNN) model and a model based on a combination of 3 DCNN and ConvLSTM (3 DCNN-ConvLSTM). In this study, we introduce a cutting-edge methodology that leverages 3 DCNN to extract intricate 3D features from medical imaging data, providing a comprehensive representation of vascular structures. Furthermore, we incorporate ConvLSTM, a specialized recurrent neural network, to capture temporal dependencies in the data, enhancing the accuracy of aneurysm detection by considering dynamic changes over time. The novelty of combining 3D convolutional neural networks (3 DCNNs) with Convolutional Long Short-Term Memory networks (ConvLSTM) lies in the enhanced capability to analyze spatiotemporal features within volumetric data. This fusion of architectures represents a significant advancement in the field of deep learning, particularly for applications dealing with three-dimensional sequences or video-like data. In summary, the novelty of combining 3D CNN with ConvLSTM lies in the holistic analysis of volumetric data, considering both spatial and temporal dimensions.