Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.

本研究针对阿尔茨海默病（AD）检测中存在的准确性和效率问题，比较了不同的深度学习模型，包括卷积神经网络、贝叶斯卷积神经网络和U-net模型，以提高识别能力。通过对MRI数据集进行严格评估，研究发现AI在AD诊断中的应用具有显著的潜力，能够推动医疗影像领域的创新及神经退行性疾病的管理。

基于深度学习的早期阿尔茨海默病检测与MRI扫描