Alzheimer's disease auxiliary diagnosis system, data processing method thereof and terminal

Abstract

The invention discloses an Alzheimer's disease auxiliary diagnosis system. The system comprises a preprocessing module which carries out preprocessing of an original MRI structure image, obtains a preprocessed image, converts the preprocessed image into an MNI space, obtains a preprocessed image, and carries out resampling of the image at an isotropic resolution; an ROI positioning module which performs rigid registration on the image obtained after preprocessing and a standard brain template to obtain a deformation field of non-rigid registration, and selects a plurality of ROIs on the preprocessed individual image according to the deformation field; and an analysis module which constructs, trains, verifies and tests a deep learning model, calculates the characteristic spectrum and the probability value of each ROI in the plurality of ROI individuals by using the deep learning model, and judges whether a subject is an Alzheimer's disease patient according to the probability values. According to the system, the deep learning technology is adopted to learn features of brain structure changes, whether a subject suffers from the Alzheimer's disease or not is predicted, the prediction accuracy is high, and an auxiliary function is provided for diagnosis of doctors.

Description

Technical field [0001] The present invention relates to the field of software technology, and more particularly to an Alzheimer's auxiliary diagnostic system, a data processing method, a terminal, and a medium. Background [0002] Alzheimer's disease is a neurological degenerative disease that causes sexually developed nervous system. Since there is no drug cure or treatment to reverse the development of disease, the medical industry will focus on early discovery and subsequent treatment. Studies have shown that in the first 20 years of dementia, the brain began to change. The existing test of Alzheimer's disease can be detected by hematology, nerve imaging examination, cerebrospinal fluid detection, gene detection, etc. These methods require professional equipment and medical staff, and detect prices are expensive, time consuming, and is not suitable for screening of large sample quantities. Inventive content [0003] For the defects in the prior art, an Alzheimer's auxiliary d...

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Problems solved by technology

These methods require professional equipment and medical staff, and the detection is expensive and time-consuming, and is not suitable for screening of large sample sizes

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