用于单视图MRI扫描阿尔茨海默病检测的CNN架构对比
A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans
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中文总结 AI 辅助
本研究对比十种CNN架构在OASIS数据集上检测阿尔茨海默病的性能，采用两阶段迁移学习，发现VGG16表现最优，且各类架构均难区分非痴呆与极轻度痴呆阶段。
中文摘要 AI 辅助
阿尔茨海默病是主要致死病因之一，目前尚无治愈方法，因此早期检测对延缓病情进展、维持生活质量至关重要。诊断依赖病史、认知测试、体格检查及脑部MRI扫描，这使得深度学习适用于阿尔茨海默病分类。本研究提出一个基准，在相同的预留测试集划分协议下评估十种不同的卷积神经网络（CNN）架构，包括ResNet、DenseNet、MobileNet、EfficientNet及VGG系列模型。采用两阶段迁移学习与全微调流程，使用从OASIS医学影像数据集提取的类别均衡子集（3900张图像）进行训练，该数据集包含86437张单视图脑部MRI扫描，标注为阿尔茨海默病的四类：非痴呆、极轻度痴呆、轻度痴呆、中度痴呆。最佳结果由VGG16实现，验证准确率为0.9637，测试准确率为0.9533。本研究记录的关键发现是，所有十种架构均一致观察到非痴呆向极轻度痴呆阶段过渡的分类难度。
英文摘要
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.
发表机构
- CETYS Universidad（塞提斯大学）
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