Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network

Yanfei Liu, Yunqiao Yang, Yi Lin, Yuexiang Li, Dong Wei, Kai Ma, Yefeng Zheng

Abstract: Subarachnoid hemorrhage (SAH) caused by the rupture of cerebral aneurysm is a serious life-threatening disease. Therefore, estimating the risk of cerebral aneurysm rupture is clinically important. In this paper, a semi-automatic method for estimating the risk of rupture of cerebral aneurysm was proposed. We applied a variety of methods to extract features of cerebral aneurysm images and 3D modeling, and used XGBoost and fully connected neural network for classification and analysis respectively. The method achieved an F2-score of 0.862 on the test set of CADA 2020.