Application of Multimodal Fusion Deep Learning Model in Disease Recognition

• URL: http://arxiv.org/abs/2406.18546v1
• Date: Wed, 22 May 2024 23:09:49 GMT
• Title: Application of Multimodal Fusion Deep Learning Model in Disease Recognition
• Authors: Xiaoyi Liu, Hongjie Qiu, Muqing Li, Zhou Yu, Yutian Yang, Yafeng Yan,
• Abstract summary: This paper introduces an innovative multi-modal fusion deep learning approach to overcome the drawbacks of traditional single-modal recognition techniques. During the feature extraction stage, cutting-edge deep learning models are applied to distill advanced features from image-based, temporal, and structured data sources. The findings demonstrate significant advantages of the multimodal fusion model across multiple evaluation metrics.
• Score: 14.655086303102575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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