用预训练模型从脑影像中自动识别阿尔茨海默病，准确率超现有方法。
Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models
- 基于VGG16等预训练模型提取脑影像特征
- 在标准数据集上达到超越当前最优的分类准确率
- 适合医学影像分析与早期疾病筛查研究者使用
阿尔茨海默病是一种不可治愈、渐进性发展的脑部疾病，会逐步剥夺患者记忆、思维能力，最终影响基本生活功能。它是老年人中最常见的痴呆原因。尽管目前尚无治愈手段，但科研人员正致力于研发治疗药物，同时已有可延缓症状的干预措施。为实现早期识别与精准诊断，全球众多研究者致力于开发计算机辅助诊断系统。本文提出一种基于神经影像生物标志物的阿尔茨海默病检测方法，采用深度学习技术从图像中提取关键视觉特征，实现疾病类别预测。实验中使用标准数据集和预训练深度学习模型，并通过标准评估指标验证性能。结果表明，基于VGG16的模型表现优于现有最先进方法。
原文摘要 · Abstract (English)
Alzheimer's disease is an untreatable, progressive brain disorder that slowly robs people of their memory, thinking abilities, and ultimately their capacity to complete even the most basic tasks. Among older adults, it is the most frequent cause of dementia. Although there is presently no treatment for Alzheimer's disease, scientific trials are ongoing to discover drugs to combat the condition. Treatments to slow the signs of dementia are also available. Many researchers throughout the world became interested in developing computer-aided diagnosis systems to aid in the early identification of this deadly disease and assure an accurate diagnosis. In particular, image based approaches have been coupled with machine learning techniques to address the challenges of Alzheimer's disease detection. This study proposes a computer aided diagnosis system to detect Alzheimer's disease from biomarkers captured using neuroimaging techniques. The proposed approach relies on deep learning techniques to extract the relevant visual features from the image collection to accurately predict the Alzheimer's class value. In the experiments, standard datasets and pre-trained deep learning models were investigated. Moreover, standard performance measures were used to assess the models' performances. The obtained results proved that VGG16-based models outperform the state of the art performance.
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