Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
<|reference_start|>Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis: 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.<|reference_end|>
@article{li2024leveraging,
    title={Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI
  Classification in Alzheimer Diagnosis},
    author={Shaojie Li, Haichen Qu, Xinqi Dong, Bo Dang, Hengyi Zang, and Yulu
  Gong},
    journal={arXiv preprint arXiv:2403.16212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16212},
    primaryClass={eess.IV cs.CV cs.LG}
}