Classification and Location of Cerebral Hemorrhage Points Based on SEM and SSA-GA-BP Neural Network

In this article, a method to fast classify (intradural hemorrhage, epidural hemorrhage, and cerebral parenchymal hemorrhage) and locate the bleeding points by using the singularity expansion method (SEM) and backpropagation (BP) neural network optimized by genetic algorithm (GA) and sparrow search algorithm (SSA) is proposed. In the simulation model, the bleeding spot with a radius of 3 mm is successfully identified by the approach. The test accuracy in the simulation for both the bleeding’s localization and classification are 98.0% and 97.4%, respectively. Head phantoms that have all been improved over the previous phantom established are used for experiments. A bleeding target with a volume of 3 mL can be identified in the microwave detection system. In the experiment, the accuracy of classification and localization of the bleeding type are 90% and 94.7%, respectively. The final results demonstrate the capability and effectiveness of the method. Faster determination of bleeding point type and orientation means that patients can be provided with different rescue measures accordingly.