Neural image classification method, computer terminal and computer readable storage medium

Abstract

The invention discloses a neural image classification method, a computer terminal and a computer readable storage medium. The method comprises the steps of generating a connection matrix correspondingto a neural image according to a connection relationship between brain regions in the neural image; extracting a predetermined number of element values from the connection matrix according to a predetermined extraction rule and forming a tested feature vector; taking the plurality of tested feature vectors as nodes, and constructing a feature map according to similar characteristics among the non-image information corresponding to each node; initializing a pre-established classification model according to the feature map, and training the initialized classification model according to the tested feature vector; and classifying the neural images according to the trained classification model. According to the technical scheme provided by the invention, the feature map is constructed throughthe similarity between the non-image information of each tested feature vector, and the established classification model is initialized according to the feature map, so that the individual differenceof the tested object and the influence of acquisition equipment on the classification result are effectively solved, and the classification performance is improved.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a neural image classification method, a computer terminal and a computer-readable storage medium. Background technique [0002] With the rapid development of medical image processing technology, the demand for classification of neuroimages (such as identifying Alzheimer's disease AD, mild cognitive impairment MCI, etc. through neural influence) is becoming more and more extensive. [0003] An existing method for classifying neuroimaging is the manual classification method. Doctors usually perform manual diagnosis based on personal medical experience combined with neuroimaging. Another method to classify neural images is a classification method based on deep learning (such as convolutional neural network). Deep learning provides a new way of thinking for graph analysis. It is not suitable for images with irregular structure and non-Euclidean space. Cont...

AI Technical Summary

Problems solved by technology

[0003] An existing method for classifying neuroimaging is the manual classification method. Doctors usually perform manual diagnosis based on personal medical experience combined with neuroimaging. Another method to classify neural images is a classification method based on deep learning (such as convolutional neural network). Deep learning provides a new way of thinking for graph analysis. It is not suitable for images with irregular structures and non-Euclidean spaces.

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