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A fusion network-based classification method for Alzheimer's disease

A technology that integrates networks and classification methods. It is applied in neural learning methods, biological neural network models, instruments, etc. It can solve the problems of poor model generalization and the accuracy rate needs to be further improved, and achieve the effect of good classification effect.

Active Publication Date: 2021-03-16
WENZHOU UNIV
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Problems solved by technology

These models that use the base network based on deep learning to achieve AD classification show good results compared with traditional machine learning methods, but there are problems with poor generalization of the model, and the accuracy rate needs to be further improved

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  • A fusion network-based classification method for Alzheimer's disease
  • A fusion network-based classification method for Alzheimer's disease
  • A fusion network-based classification method for Alzheimer's disease

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Embodiment Construction

[0026] In order to describe the technical solutions in the embodiments of the present invention completely and clearly, further details will be described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0027] see figure 1 , the present invention provides a technical solution: a fusion network-based Alzheimer's disease classification method, comprising the following steps:

[0028] Step S1: Obtain the subject's magnetic resonance image dataset from the public dataset of ADNI (http: / / adni.loni.usc.edu / ), which is a t1-weighted structural MRI scan, after specific image preprocessing steps , including multiplanar reconstruction (MPR), grayscale bias (GradWarp), B1 non-uniformity correction, and N3 intensity normalization. The dataset contains MRI images and subject labels, where labels are e...

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Abstract

The invention discloses a method for classifying Alzheimer's disease based on a fusion network, which comprises the following steps: 1. Preprocessing a given magnetic resonance image data set of a subject; 2. Dividing samples into a training set and a test set, Perform image amplification and normalization operations; 3. Input the training set into the training network; 4. Perform feature extraction and feature fusion on the samples; 5. Fuse the classification decision of each base network and the classification decision of feature fusion; 6. Calculate the error of the output label and update the parameters through backpropagation; 7. Evaluate the classification model and obtain the optimal model, repeat steps 3‑7 until the end of the iteration; The subject's label was obtained by processing the MRI data of the subject. The invention uses a convolutional neural network to realize a method for effectively using the magnetic resonance image data of the subjects to classify Alzheimer's disease, and has better robustness.

Description

technical field [0001] The invention belongs to the interdisciplinary field of brain imaging and computer science, relates to the technical field of image classification, and in particular relates to a fusion network-based classification method for Alzheimer's disease. Background technique [0002] With the rapid development of artificial intelligence and neuroimaging technology, medical image processing, as one of the fields most closely related to medical science and technology, is booming. The classification of Alzheimer's disease is an important research direction in the field of medical image classification, and it is of great significance in computer-aided diagnosis. The analysis of magnetic resonance images by machine learning can realize the rapid and accurate classification of Alzheimer's disease (AD). In some cases, this approach demonstrated better classification accuracy than clinicians. Computer-aided diagnosis based on machine learning has developed into an i...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/251G06F18/254G06F18/253
Inventor 胡众义陈昌足吴奇
Owner WENZHOU UNIV