Age estimation method based on deep learning

Abstract

The invention discloses an age estimation method based on deep learning. The method comprises the following steps: firstly, acquiring brain nuclear magnetic resonance images of a plurality of persons,taking the brain nuclear magnetic resonance images as training samples, segmenting the brain nuclear magnetic resonance images as the training samples, extracting and digitalizing the segmented imageslices of each block to obtain image data of the training samples, and preprocessing the image data to obtain a mean value and a variance; establishing a convolutional neural network, obtaining an input value of the convolutional neural network, carrying out training classification on the convolutional neural network to obtain a trained model, and then obtaining a relationship between proportionsof all block areas of the human brain nuclear magnetic resonance image; and finally, taking the brain nuclear magnetic resonance image of the subject as a test sample, substituting the test sample into the model to obtain a feature vector, and sending the feature vector into a support vector machine for training classification to obtain the predicted age of the test sample. According to the method, the age of the subject can be estimated more quickly, efficiently and accurately.

Description

technical field [0001] The present invention relates to the technical field of age estimation methods, in particular to an age estimation method based on deep learning. Background technique [0002] As humans age, the structure of the human brain changes accordingly. Studies have shown that neurodegenerative diseases such as Alzheimer's disease (AD) are associated with autophagy, which often leads to brain shrinkage. By comparing chronological age with age estimated from brain MRIs, computers can help determine whether someone is suffering from AD. In order to identify possible patients as early as possible, a more accurate prediction method is needed. [0003] For human brain images, it can be further segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. Most existing age prediction methods take MRI images of one or more regions as input, and the calculation results are often inaccurate. At the same time, as the number of selected reg...

AI Technical Summary

Problems solved by technology

Most existing age prediction methods take MRI images of one or more regions as input, and the calculation results are often inaccurate

At the same time, during execution, as the number of selected regions increases, the steps will be more cumbersome

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