Abstract
OBJECTIVE: In order to study the application of multimodal neuroimaging in the treatment of neurological patients, the brain of patients was scanned and identified through multimodal neuroimaging, so as to provide basis for doctors to judge their diseases and give treatment plans. METHOD: Understand the principle of multimodal neural scene currently used through literature analysis, analyze the current level of neurological diseases in the medical field and the application of multimodal neural image in diseases, recalculate the imaging examination data through neurofuzzy algorithm, and obtain a more optimized identification method than the previous visual judgment method. RESULT: The image inspection data is dimensioned through computer spatial convolution. At this time, the output two-dimensional array is not accurate enough. It needs to be processed again through fuzzy spatial convolution to obtain relatively accurate data output, generate a small two-dimensional array, and use the convolution core for the spatial convolution process of two-dimensional array. The algorithm uses neural network machine learning algorithm to identify and judge the inspection data. CONCLUSION: Through this algorithm to identify the examination data, the early diagnosis sensitivity of intracranial space occupying lesions and intracranial hematogenous lesions are more than 20% higher than the previous traditional recognition methods, which provides a medical imaging basis for the early diagnosis and treatment of acoustic diseases, and improves the treatment probability and prognosis quality of life of neurological patients.