Application of Multimodal Fusion Deep Learning Model in Disease Recognition

Understand

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.