Research on the Recognition of Mild Cognitive Impairment Based on Deep Learning
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Abstract
With the accelerating process of global population aging, the importance of early diagnosis and intervention for mild cognitive impairment (MCI), as a prodromal stage of dementia, has become increasingly prominent. Accurate early identification of MCI is the key to delaying disease progression and reducing the incidence of senile dementia. Traditional recognition methods mostly rely on scale assessment and clinical experience of doctors, which have problems such as strong subjectivity, low efficiency, and insufficient standardization. Deep learning technology has powerful capabilities in automatic feature extraction and pattern recognition, and can mine hidden features in medical images, physiological signals, and behavioral data. This paper takes deep learning algorithms as the core to carry out research on automatic recognition of MCI. It sorts out the pathogenesis characteristics and existing recognition methods of MCI, constructs recognition models based on convolutional neural networks and recurrent neural networks, and uses public medical datasets to complete model training and verification. A series of comparative experiments are designed, and four evaluation indicators are used to test the recognition accuracy and stability of the models. The results show that deep learning models can effectively extract features related to cognitive impairment, and their recognition accuracy for MCI is better than traditional manual assessment and machine learning methods. This study can provide technical reference for non-invasive clinical screening and intelligent auxiliary diagnosis, as well as theoretical support for the construction of an early warning system for geriatric neurological diseases. The EEG preprocessing and temporal modeling connect the recognition framework with biomedical bioelectrical signal analysis.
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