Alzheimers 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 Alzheimeru0027s disease(AD). Nowadays, Alzheimeru0027s 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 Alzheimeru0027s disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimeru0027s disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimeru0027s disease and normal, and the chance of it turning into Alzheimeru0027s 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 Alzheimeru0027s disease accurately, it will be beneficial for the early diagnosis of Alzheimeru0027s disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimeru0027s disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.

RNNBased Alzheimers Disease Prediction from Prodromal Stage using Diffusion Tensor Imaging Alzheimeru0027s Disease is an irreversible, progressive brain disorder that slowly destroys cognitive abilities. In recent years, the relationship between the prodromal Mild Cognitive Impairment (MCI) stage and the Alzheimeru0027s Disease (AD) stage has been extensively researched in hopes of finding a path towards early diagnosis. Early detection at the MCI stage can help determine appropriate treatment plans as well as assist in clinical trial enrollment as 32% of individuals with MCI will develop AD within 5 years. Computer vision studies leveraging Magnetic Resonance Imaging (sMRI, fMRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET) have led to encouraging results in classifying the different stages of AD. Studies around DTI specifically have shown that structural differences in white matter are prevalent between these stages. Rather than classification between stages, we propose a recurrent neural network model (RNN) based on the DTI modality for identifying the subset (32%) of individuals with Early Mild Cognitive Impairment (EMCI) that will develop AD. Our results are stateoftheart and demonstrate high accuracy in determining which individuals will develop AD within the next 57 years. Additionally, we propose our augmentation methods for DTI data as well as our classification accuracy across the traditional AD stage categories.