This paper introduces an innovative multi-modal fusion deep learning approach
to overcome the drawbacks of traditional single-modal recognition techniques.
These drawbacks include incomplete information and limited diagnostic accuracy.
During the feature extraction stage, cutting-edge deep learning models
including convolutional neural networks (CNN), recurrent neural networks (RNN),
and transformers are applied to distill advanced features from image-based,
temporal, and structured data sources. The fusion strategy component seeks to
determine the optimal fusion mode tailored to the specific disease recognition
task. In the experimental section, a comparison is made between the performance
of the proposed multi-mode fusion model and existing single-mode recognition
methods. The findings demonstrate significant advantages of the multimodal
fusion model across multiple evaluation metrics.

本文介绍了一种创新的多模态融合深度学习方法，以克服传统单模态识别技术的缺点，包括信息不完整和诊断准确性有限。通过应用先进的深度学习模型（包括卷积神经网络（CNN），循环神经网络（RNN）和变换器）从基于图像、时间和结构化数据源提取高级特征。融合策略组件旨在确定适合特定疾病识别任务的最佳融合模式。在实验部分，我们比较了拟议的多模态融合模型与现有的单模态识别方法的性能。研究结果表明，在多个评估指标上，多模态融合模型具有显著的优势。