access icon free Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging (MRI). In order to improve the accuracy of MRI in detecting brain information in patients with schizophrenia, this study is based on the support vector machine classification algorithm and combined with multimodal MRI detection method to construct a detection model suitable for patients with schizophrenia. In addition, this study combines the existing test cases to divide the brain into regions and design a comparative experiment to study the accuracy of the model proposed in this study. Finally, the study draws the results by sub-regional comparison. Studies have shown that the algorithm model of this study has certain effects on brain detection in patients with schizophrenia, and can be applied to practice, and can provide theoretical reference for subsequent related research.

Inspec keywords: biomedical MRI; medical image processing; support vector machines; brain

Other keywords: brain detection; existing test cases; detection model; traditional magnetic resonance imaging; complex environmental effects; brain information; support vector machine classification algorithm; normal human brain avatar; algorithm model; schizophrenic patients; multimodal magnetic resonance imaging; multimodal MRI detection method; schizophrenia

Subjects: Optical, image and video signal processing; Biomedical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Medical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation; Biology and medical computing; Other topics in statistics; Probability theory, stochastic processes, and statistics

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