Design and optimization of an accurate diagnostic system for spinal cord injury by integrating multimodal images

In order to improve the accurate diagnosis of spinal cord injury, a diagnostic system that fuses multimodal medical images is designed. Based on T1WI, T2WI, DTI and CT image data, a feature fusion model based on the attention mechanism is constructed, and the deep integration of multimodal information is realized through multi-channel feature alignment and cross-modal interactive attention module. Multi-task learning mechanism is introduced to jointly predict the injury site and lesion type, and Focal Loss is used to optimize the recognition ability of the model for a few types of samples. The results show that the system is superior in terms of inference delay, accuracy and memory consumption, especially robust in feature extraction in complex lesion regions, which provides strong support for accurate diagnosis and personalized treatment of spinal cord injury.