Classification of Alzheimer’s disease based on deep learning

Alzheimer’s disease(AD), commonly known as senile dementia, is an irreversible and fatal chronic neurological disease. At present, there are 50 million people with Alzheimer’s disease in the world, and the number of patients is increasing year by year. The disease develops slowly, and there is no clinical solution that can completely cure the disease, only in the prodromal stage of the disease (Mild cognitive impairment, MCI) through some drugs or psychological intervention to reduce symptoms and delay the disease. Therefore, it is very important to diagnose the disease and its prodromal stage through medical imaging technology, which has great clinical significance for the subsequent treatment of patients. Functional Magnetic Resonance Imaging (fMRI) technology has been introduced as a non-invasive, high-resolution neuroimaging technology, and has excellent performance in the diagnosis of Alzheimer’s disease. With the continuous development of artificial intelligence, machine learning has become a major research hotspot. As a major branch of machine learning, deep learning is widely used in the field of medical images. Therefore, this paper combines fMRI images and a deep learning-based method to predict the relevant classification of three stages of Alzheimer’s disease (AD, MCI, and Normal cognitive (NC)).