# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 1 Introduction  
In recent years, deep learning approaches, particularly convolutional neural networks (CNNs), have demonstrated significant potential for detecting brain aneurysms from medical images. In our research, we aim to explore the use of 2 DCNNs for aneurysm detection, with the goal of enhancing the accuracy and efficiency of existing detection methods. A brain aneurysm refers to a bulge or ballooning in a blood vessel in the brain, often resembling a berry hanging on a stem. If an aneurysm ruptures, it can lead to bleeding in the brain, becoming a life-threatening condition that requires prompt medical treatment. However, most brain aneurysms do not rupture, cause health problems, or exhibit symptoms. These aneurysms are typically detected incidentally during tests conducted for other medical conditions. In certain instances, treatment for an unruptured brain aneurysm may be deemed appropriate and could potentially prevent a rupture in the future. Brain aneurysms are enormous threats to human health, with a prevalence of approximately 4% [1,2]. The rupture of an aneurysm usually causes death or severe damage to the patients. Brain aneurysms are potentially life-threatening conditions that require early detection and proper treatment to prevent serious consequences such as stroke, hemorrhage, or even death [3]. Brain aneurysm detection from medical images such as computed tomography(CT) and magnetic resonance imaging (MRI) scans is challenging. It requires accurate and reliable analysis of complex data. Both MRI and CT scans can view internal body structures [4]. Nevertheless, a CT scan is quicker and can generate images of tissues, organs, and skeletal structures. On the other hand, an MRI excels at capturing detailed images that assist doctors in identifying the presence of abnormal tissues within the body. MRIs provide a higher level of detail in their scan images. Deep neural networks (DNNs) have emerged as promising approaches for detecting brain aneurysms. DNNs are machine learning algorithms capable of automatically learning and extracting relevant features from large datasets, enabling accurate and efficient analysis of medical images. In this article, we will delve into the state-of-the-art approaches in brain aneurysm detection using DNNs, including convolutional neural networks (CNNs), transfer learning, and ensembling techniques. We will also discuss the various types of medical images employed for brain aneurysm detection and the potential benefits of using DNNs for segmentation. Overall, this article aims to provide an overview of the latest developments in brain aneurysm detection using DNNs and their potential to enhance patient outcomes. The detection of aneurysms is a time-consuming and technically demanding task. It uses a computed tomography aneurysm (CTA) machine. It uses X-rays to scan parts of the human body from multiple angles and multiple spatial projections, respectively. [PERSON] compares different CTA-based aneurysm analysis techniques [5]. It is laborious and time-consuming work. The radiologist specialist must analyze hundreds of images carefully for every single patient. He must explore details from different views to determine whether they are normal blood vessels or contain aneurysms. However, the small size of aneurysms and low-intensity contrast to normal vessels requires a sub-specially trained radiologist specialist. [PERSON] et al. [6] investigated how computer-assisted detection of intracranial aneurysms (IAs) using a deep neural network can improve the efficiency and speed of analysis. He utilized the GLIA-Net model for CTA image segmentation. His research showed that Artificial Inference (AI) detects aneurysms approximately 6 times faster than specialized radiologists. The low accuracy of the automatic detection--it hovered around 20%, while a human worked with an accuracy of around 90%--emerged as a shortcoming of AI. AI was able to achieve an average recall of 85% on average.