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.

利用深度学习模型通过创新的数据处理和模型构建步骤对核磁共振成像（MRI）图像进行分类，以识别阿尔茨海默病的不同阶段，并通过非侵入性手段实现疾病进展的监测和早期精确诊断，实验证明基于Xception模型的深度学习框架在多类别MRI图像分类任务中达到了99.6%的准确率，具有辅助诊断的潜在应用价值。未来的研究将侧重于扩大数据集、改进模型可解释性和临床验证，进一步推动深度学习技术在医学领域的应用，希望为阿尔茨海默病患者带来更早的诊断和个性化治疗方案。

利用深度學習和Xception架構進行高準確性MRI在阿茲海默症診斷中的分類