A method for Parkinson's disease classification and lesion area labeling in MRI images
A Parkinson's disease and image technology, applied in the field of medical images of computer vision, can solve the problems of inaccurate lesion area and poor classification results, and achieve the effects of improving training efficiency, reducing dimensions, and reducing gradient disappearance.
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[0044] Specific examples of the present invention are given below to further illustrate the present invention.
[0045] S1: Download the medical image dataset, extract 1557 PD (Parkinson's disease) images, 543 Control (normal) images, and 193 Prodromal (latency) images according to the label documents.
[0046] S2: Preprocess the MRI images. For images that do not meet the resolution, use the cubic interpolation method and average pooling to perform upsampling and downsampling respectively, and then obtain the training set and save it in tfrecord format.
[0047] Downsampling uses the average pooling method, that is, only averages the feature points in the neighborhood. The formula is:
[0048] alpha i ∈{0,1},
[0049]
[0050]
[0051] Upsampling uses cubic interpolation.
[0052] S3: Construct classification module, such as figure 1 shown. The input is a tensor of [224,224,3], through the first convolutional layer (conv), the convolution kernel size is 7*7, and th...
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