使用简单和极小的卷积神经网络来对阿尔茨海默病进行分类的极简方法
Edvard O S Grødem1, Esten Leonardsen2, Bradley J MacIntosh3
1Computational Radiology & Artificial Intelligence unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0372, Oslo, Norway; Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Oslo, Norway.
Journal of neuroscience methods
|August 21, 2024
概括
一个简单的3D卷积神经网络 (SFCN) 从MRI扫描中有效地识别阿尔茨海默病 (AD),其性能优于EfficientNet.Net等复杂模型. 这表明更简单的深度学习模型对于AD诊断是有效的.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 深度学习模型对识别阿尔茨海默病 (AD) 使用神经成像数据如MRI具有广泛的兴趣.
- 对像EfficientNet这样的现代架构与AD分类的标准架构进行调查至关重要.
研究的目的:
- 评估是否像EfficientNet这样的先进架构在AD识别中比标准模型提供了性能优势.
- 为了评估AD分类的最小3D卷积神经网络 (SFCN) 的性能.
主要方法:
- 利用了来自阿尔茨海默病神经成像计划 (ADNI) 的T1加权MRI数据,并进行了最小的预处理.
- 测试了一个简单的3D卷积神经网络 (SFCN) 架构及其缩放版本 (720到2.9M参数).
- 将SFCN与DenseNet,EfficientNet和现有文献结果进行比较.
主要成果:
- 在测试中,SFCN的ROC AUC达到96.0%,超过了EfficientNet的94.9%.
- 即使只有720个可训练参数,SFCN也保持了高性能 (91.4%的ROC AUC).
- 最小的预处理管道与SFCN相结合,展示了强大的分类能力.
结论:
- 具有基本预处理的最小3D CNN (SFCN) 实现了对AD分类的竞争性性能.
- 挑战了基于神经成像的AD诊断中复杂,参数重的架构的需求.
- 突出了更简单的深度学习模型用于AD诊断的效率和潜力.
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