Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI
  Classification in Alzheimer Diagnosis
Shaojie Li Haichen Qu Xinqi Dong Bo Dang Hengyi Zang Yulu Gong
Exploring the application of deep learning technologies in the field of
medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique
perspective for observing and diagnosing complex neurodegenerative diseases
such as Alzheimer Disease (AD). With advancements in deep learning,
particularly in Convolutional Neural Networks (CNNs) and the Xception network
architecture, we are now able to analyze and classify vast amounts of MRI data
with unprecedented accuracy. The progress of this technology not only enhances
our understanding of brain structural changes but also opens up new avenues for
monitoring disease progression through non-invasive means and potentially
allows for precise diagnosis in the early stages of the disease.
  This study aims to classify MRI images using deep learning models to identify
different stages of Alzheimer Disease through a series of innovative data
processing and model construction steps. Our experimental results show that the
deep learning framework based on the Xception model achieved a 99.6% accuracy
rate in the multi-class MRI image classification task, demonstrating its
potential application value in assistive diagnosis. Future research will focus
on expanding the dataset, improving model interpretability, and clinical
validation to further promote the application of deep learning technology in
the medical field, with the hope of bringing earlier diagnosis and more
personalized treatment plans to Alzheimer Disease patients.