基于MRI扫描的阿尔茨海默病早期检测深度学习方法
Deep Learning for Early Alzheimer Disease Detection with MRI Scans
AI总结：
本研究针对40岁以上人群的阿尔茨海默病，基于OASIS脑部MRI数据集对比卷积神经网络、贝叶斯卷积神经网络及U-net模型，解决数据不平衡问题并通过多指标评估模型优劣，为AI辅助AD诊断及医学影像相关创新提供参考。
摘要：
阿尔茨海默病是一种以痴呆和神经功能受损为特征的神经退行性疾病。本研究主要聚焦40岁以上人群，该病会影响他们的记忆、行为以及大脑认知过程。阿尔茨海默病需要通过详细评估患者的MRI（磁共振成像）扫描结果和神经心理学测试来诊断。本项目旨在提升AD（阿尔茨海默病）诊断的准确率与效率，对现有深度学习模型进行对比，重点研究了Convolutional Neural Network（卷积神经网络）、Bayesian Convolutional Neural Network（贝叶斯卷积神经网络）以及U-net模型，所用数据集为Open Access Series of Imaging Studies（成像研究开放获取系列）脑部MRI数据集。此外，为确保模型评估的鲁棒性与可靠性，我们解决了数据不平衡的挑战。随后我们通过考量灵敏度、特异度和计算效率开展了严格评估，以确定每种模型的优缺点。该对比分析不仅阐明了AI在革新AD诊断方面的未来作用，还为医学成像和神经退行性疾病管理领域的未来创新铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
