Automated Detection of Cerebral Aneurysms using Deep Learning Techniques

Detecting brain aneurysms is a critical area of research due to the potential life-threatening consequences of aneurysm rupture. Early detection of a cerebral (brain) aneurysm can increase the chances of successful treatment. In this paper, we explore various neural network architectures such as 2DCNN, PointCNN, PointNet and 3DCNN for classification of cerebral aneurysms. Our main objective is to develop a reliable system that enhances the accuracy of aneurysm detection by medical professionals. Ultimately, the goal is to contribute towards improving patient outcomes and saving lives. Our experimental results, which were obtained using the IntrA: 3D Intracranial Aneurysm dataset, indicate the superiority of the proposed 2DCNN and 3DCNN for detection of brain aneurysms. We compare the results with other deep learning architectures. Our proposed 2DCNN and 3DCNN architectures achieved precision of 85.91% and 89.05%, respectively.