Method and device for constructing brain disease classification model and intelligent terminal

Abstract

The invention provides a method and device for constructing a brain disease classification model, and an intelligent terminal. The brain disease classification model construction method comprises the steps of obtaining Rs-fMRI image data from multiple brain disease data sets; based on a plurality of preset brain templates, preprocessing the Rs-fMRI image data to obtain an average time sequence; constructing a functional connection network corresponding to each brain disease data set and the brain template according to the average time sequence, and extracting features from the functional connection network to obtain a feature matrix; constructing a plurality of initialized brain disease classification models corresponding to each brain disease data set according to a preset sparse learning model, a regularization item and a feature matrix; and performing integrated learning on the plurality of initialized brain disease classification models, and determining a target brain disease classification model corresponding to each brain disease data set. According to the method, the constructed brain function connection network has more physiological significance, the problem of high heterogeneity in data can be effectively solved, and the universality of a brain disease classification model is improved.

Description

technical field [0001] The invention relates to the technical field of brain disease diagnosis, in particular to a method, device and intelligent terminal for constructing a brain disease classification model. Background technique [0002] ASD (Autism spectrum disorder, autism spectrum disorder) is a group of rapidly developing and highly heterogeneous neurodevelopmental disorders. It has been proposed to apply machine learning technology to the diagnosis of ASD. However, in the prior art, when constructing a brain disease classification model It has strong limitations, such as building a brain disease classification model based on a single brain template, building a brain disease classification model based on a single imaging center, or building a brain disease classification model based on multiple brain templates in a single imaging center. The brain disease classification obtained by these methods The model has low generalizability, leading to significant heterogeneity i...

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Problems solved by technology

[0002] ASD (Autism spectrum disorder, autism spectrum disorder) is a group of rapidly developing and highly heterogeneous neurodevelopmental disorders. It has been proposed to apply machine learning technology to the diagnosis of ASD. However, in the prior art, when constructing a brain disease classification model It has strong limitations, such as building a brain disease classification model based on a single brain template, building a brain disease classification model based on a single imaging center, or building a brain disease classification model based on multiple brain templates in a single imaging center. The brain disease classification obtained by these methods Models have low generalizability, leading to significant heterogeneity issues when applying brain disease classification models to other imaging centers

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