用深度学习从MRI图像中早期识别阿尔茨海默病，准确率达98.67%。
Early diagnosis of Alzheimer's disease from MRI images with deep learning model
- 结合SMOTE与预训练网络提取病变特征
- 在不平衡数据集上实现98.67%分类准确率
- 适合医疗影像诊断与早期筛查研究者
阿尔茨海默病（AD）是全球痴呆症最常见的病因，病情由轻至重发展并影响日常活动。早期诊断对患者护理和临床试验至关重要。本文利用卷积神经网络（CNN）构建框架，从磁共振成像（MRI）扫描中识别疾病特征。尽管现有方法包括病史回顾、神经心理测试和MRI检查，但来自Kaggle的数据集存在严重的类别不平衡问题，需确保各类别样本均衡分布。为此，本文采用合成少数类过采样技术（SMOTE）解决该问题，并将预训练卷积神经网络应用于DEMNET痴呆网络以提取AD图像的关键特征。所提模型在测试中达到98.67%的准确率。
原文摘要 · Abstract (English)
It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%.
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