Wu Qi, Wang Yannan, Zhang Xiaojuan, Zhang Hongqiang, Che Kuanyu
Department of Psychiatry, The Third People's Hospital of Lanzhou, Lanzhou 730030, Gansu, China.
Department of Pediatric Psychiatry, The Third People's Hospital of Lanzhou, Lanzhou 730030, Gansu, China.
Bioimpacts. 2025 Apr 12;15:30849. doi: 10.34172/bi.30849. eCollection 2025.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses significant challenges for early detection. Advanced diagnostic methods leveraging machine learning techniques, particularly deep learning, have shown great promise in enhancing early AD diagnosis. This paper proposes a multimodal approach combining transfer learning, Transformer networks, and recurrent neural networks (RNNs) for diagnosing AD, utilizing MRI images from multiple perspectives to capture comprehensive features.
Our methodology integrates MRI images from three distinct perspectives: sagittal, coronal, and axial views, ensuring the capture of rich local and global features. Initially, ResNet50 is employed for local feature extraction using transfer learning, which improves feature quality while reducing model complexity. The extracted features are then processed by a Transformer encoder, which incorporates positional embeddings to maintain spatial relationships. Finally, 2D convolutional layers combined with LSTM networks are used for classification, enabling the model to capture sequential dependencies in the data.
The proposed framework was rigorously tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our approach achieved an impressive accuracy of 96.92% on test data and 98.12% on validation data, significantly outperforming existing methods in the field. The integration of Transformer and LSTM models led to enhanced feature representation and improved diagnostic performance.
This study demonstrates the effectiveness of combining transfer learning, Transformer networks, and LSTMs for AD diagnosis. The proposed framework provides a comprehensive analysis that improves classification accuracy, offering a valuable tool for early detection and intervention in clinical practice. These findings highlight the potential for advancing neuroimaging analysis and supporting future research in AD diagnostics.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，对早期检测构成重大挑战。利用机器学习技术，特别是深度学习的先进诊断方法，在增强AD早期诊断方面显示出巨大潜力。本文提出了一种结合迁移学习、Transformer网络和循环神经网络（RNN）的多模态方法来诊断AD，从多个角度利用MRI图像来捕捉全面特征。
我们的方法从三个不同角度整合MRI图像：矢状面、冠状面和轴位视图，确保捕捉丰富的局部和全局特征。最初，使用ResNet50通过迁移学习进行局部特征提取，这在降低模型复杂度的同时提高了特征质量。然后，提取的特征由Transformer编码器处理，该编码器结合位置嵌入以保持空间关系。最后，将二维卷积层与LSTM网络结合用于分类，使模型能够捕捉数据中的序列依赖性。
所提出的框架在阿尔茨海默病神经影像倡议（ADNI）数据集上进行了严格测试。我们的方法在测试数据上达到了令人印象深刻的96.92%的准确率，在验证数据上达到了98.12%的准确率，显著优于该领域的现有方法。Transformer和LSTM模型的整合导致了增强的特征表示和改进的诊断性能。
本研究证明了结合迁移学习、Transformer网络和LSTM用于AD诊断的有效性。所提出的框架提供了全面分析，提高了分类准确率，为临床实践中的早期检测和干预提供了有价值的工具。这些发现突出了推进神经影像分析和支持AD诊断未来研究的潜力。
