对比10种CNN模型，发现VGG16在阿尔茨海默病早期筛查中表现最佳。
A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans
- 统一测试协议下比较10种CNN架构，采用迁移学习与全微调流程。
- VGG16达95.33%测试准确率，但轻度痴呆阶段识别仍困难。
- 适合关注医学影像分析与早期神经退行性疾病检测的研究者。
阿尔茨海默病是导致死亡的主要原因之一，目前尚无治愈方法，因此早期检测对延缓病情进展、提升生活质量至关重要。诊断依赖于病史、认知测试、体格检查和脑部MRI扫描，深度学习在此类分类任务中具有天然优势。本文构建基准，评估十种卷积神经网络（CNN）架构（包括ResNet、DenseNet、MobileNet、EfficientNet及VGG系列模型）在相同保留测试集协议下的性能。采用基于OASIS医学影像数据集的类别平衡子集（共3,900张图像），该数据集包含86,437例单视角脑MRI扫描，分为四类：非痴呆（Non-Demented）、极轻度痴呆（Very Mild Dementia）、轻度痴呆（Mild Dementia）和中度痴呆（Moderate Dementia）。训练流程结合两阶段迁移学习与全微调策略。最优结果由VGG16实现，验证准确率达0.9637，测试准确率为0.9533。关键发现是：所有十种架构均一致表现出从非痴呆向极轻度痴呆过渡阶段识别困难。
原文摘要 · Abstract (English)
Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life. Diagnosis relies on medical history, cognitive tests, physical exams, and MRI brain scans, making deep learning suitable for Alzheimer's classification. This work proposes a benchmark that evaluates ten different convolutional neural network (CNN) architectures (including ResNet, DenseNet, MobileNet, EfficientNet, and VGG family models) under the same held-out test split protocol. A two-stage transfer learning and full fine-tuning pipeline is introduced to perform training using a class-balanced subset (3,900 images) derived from the OASIS medical imaging dataset, comprising 86,437 single-view MRI brain scans labeled into four classifications of Alzheimer's disease: Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The best results were achieved by VGG16, with a 0.9637 validation accuracy and a 0.9533 test accuracy score. A key finding documented in this work is the difficulty of classifying the transition from Non-Demented to Very Mild Demented stages, observed consistently across all ten architectures.
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