Application of Multimodal Fusion Deep Learning Model in Disease Recognition
<|reference_start|>Application of Multimodal Fusion Deep Learning Model in Disease Recognition: 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.<|reference_end|>
@article{liu2024application,
    title={Application of Multimodal Fusion Deep Learning Model in Disease
  Recognition},
    author={Xiaoyi Liu, Hongjie Qiu, Muqing Li, Zhou Yu, Yutian Yang, Yafeng Yan},
    journal={arXiv preprint arXiv:2406.18546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18546},
    primaryClass={cs.CV cs.AI}
}