Alzheimer's disease classification method based on depth forest
A classification method and forest technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of difficult MRI image features, low classification accuracy of Alzheimer's disease, etc., to achieve early diagnosis, good description of samples, The effect of improving the recognition rate
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[0037] A classification method of Alzheimer's disease based on deep forest includes the following steps:
[0038] Step 1: Use the MRI image for detecting Alzheimer's disease as the input of the multi-granularity scan, the output of the multi-granularity scan is connected to the cascade forest, and the cascade forest outputs a class vector of the MRI image to complete the deep forest model Constructed, the classification results of the MRI image include Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal people (NC), and the class vector is the maximum probability value in the MRI image class.
[0039] Step 2: Preprocess the MRI images of several known categories, that is, perform AC-PC origin correction on the MRI image; segment each corrected MRI image to obtain an MRI gray matter image; normalize the MRI gray matter image to A unified MNI template makes the size of each MRI gray matter image uniform; smoothing and down-sampling the standardize...
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Specific Example 2
[0042] Step 1: Use the MRI image for detecting Alzheimer's disease as the input of the multi-granularity scan, the output of the multi-granularity scan is connected to the cascade forest, and the cascade forest outputs a class vector of the MRI image to complete the deep forest model Constructed, the categories of the MRI image include Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal people (HC), and the class vector is the maximum probability value in the MRI image category.
[0043] Step 2: Preprocess the MRI images of several known categories, that is, perform AC-PC origin correction on the MRI image; segment each corrected MRI image to obtain an MRI gray matter image; normalize the MRI gray matter image to A unified MNI template makes the size of each MRI gray matter image uniform; smoothing and down-sampling the standardized MRI gray matter image, and slicing the processed image to obtain a preprocessed MRI image. Divided into training...
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