Alzheimer's Disease Diagnosis Using Enhanced Inception Network Based on Brain Magnetic Resonance Image

An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease(AD). Nowadays, Alzheimer's disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimer's disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimer's disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimer's disease and normal, and the chance of it turning into Alzheimer's disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimer's disease accurately, it will be beneficial for the early diagnosis of Alzheimer's disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimer's disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.