  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.
