用深度学习分析MRI，提升阿尔茨海默病早期诊断准确率。
Deep Learning for Early Alzheimer Disease Detection with MRI Scans
- 对比CNN、Bayesian CNN和U-net三种模型在MRI上的表现。
- 在OASIS数据集上实现高敏感性与特异性，克服数据不平衡问题。
- 为医疗影像AI诊断提供可落地的模型选择参考。
阿尔茨海默病是一种以认知功能衰退和神经功能障碍为特征的神经退行性疾病，主要影响40岁以上人群的记忆、行为和脑部认知过程。其诊断依赖于详细的MRI扫描与神经心理学测试。本研究针对开放获取成像研究系列（OASIS）脑部MRI数据集，比较卷积神经网络（CNN）、贝叶斯卷积神经网络及U-net模型在提升阿尔茨海默病诊断准确性与效率方面的表现。为确保评估稳健可靠，特别处理了数据不平衡问题，并通过敏感性、特异性与计算效率等指标进行严格评估，全面分析各模型的优势与局限。该对比研究不仅揭示了人工智能在阿尔茨海默病诊断中的潜力，也为医学影像分析与神经退行性疾病管理的未来发展提供了新路径。
原文摘要 · Abstract (English)
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.
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