# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 7 Conclusions
Automated detection through deep neural networks can significantly reduce the manual workload of healthcare professionals. By assisting in preliminary analysis, the method allows medical experts to focus on in-depth evaluation and decision making. The use of deep neural networks contributes to the speed and efficiency of the diagnostic process. Rapid identification of brain aneurysms can be pivotal in emergency situations or time-sensitive medical conditions. The approach can be adapted to various imaging modalities, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans, making it versatile for different medical imaging scenarios. The deep neural network aspect implies the potential for continual learning and improvement. As the model encounters more data, it can adapt and refine its detection capabilities, staying current with evolving medical imaging practices. The method can contribute to ongoing research and development efforts in the field of medical image analysis and neural network applications, fostering innovation and advancements in healthcare technologies. In conclusion, aneurysm classification using deep neural networks has shown promising results in recent years. However, there are still some challenges that need to be addressed, such as the lack of annotated medical images and the class imbalance problem. Future research can focus on developing more robust deep neural networks, exploring the use of multimodal imaging, and developing more comprehensive datasets of annotated medical images. In our future work, we would like to focus on the semi-automated analysis of CTA images to calculate the probability of aneurysms, where the physician could check for highlighted potential aneurysm areas in the first step. We also wanted to try to compute a 3D model from the images and test the proposed procedures with 3D-CNNs, possibly trying to train the models on already pre-trained networks. One direction for future research is to develop more robust deep neural networks that can handle noise and artifacts in medical images. Another direction is to explore the use of multimodal imaging for aneurysm classification, where multiple types of medical images, such as CT and MRI, are used together to improve the accuracy of the classification. Finally, the development of more comprehensive datasets of annotated medical images can help to improve the performance of deep neural networks in aneurysm classification. Conceptualization, M.P. and P.K. ; methodology, M.P., P.K. and R.H.; software, M.P. ; validation, P.K., M.P., R.H. and K.Z. ; formal analysis, P.K., M.P., R.H. and K.Z. ; investigation, M.P. and R.H.; resources, M.P. and K.Z. ; writing--original draft preparation, P.K. and M.P. ; writing--review and editing, M.P. and P.K. ; visualization, M.P. and P.K. ; supervision, R.H.; project administration, R.H. and P.K. ; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript. This work was supported by a Slovak Research and Development Agency under contract No. APVV-21-0502: BrainWatch: System for automatic detection of intracranial aneurysms and with the support of the Integrated Infrastructure Operational Program for the project New possibilities for the management of serious diseases in medical and preventive care with regard to the safety of health professionals, ITMS: 313011 AUAS, co-financed by the European Regional Development Fund. **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by ethical committee of University Hospital Martin (EK UNM 10/2023). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data presented in this study are available on request from the authors. This is according to the laboratory rules.