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arXiv · 2406.18546

Application of Multimodal Fusion Deep Learning Model in Disease Recognition

Xiaoyi Liu·Hongjie Qiu·Muqing Li·Zhou Yu·Yutian Yang·Yafeng Yan
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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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Xiaoyi Liu, Hongjie Qiu, Muqing Li, Zhou Yu, Yutian Yang, Yafeng Yan. 2024-05-22. Application of Multimodal Fusion Deep Learning Model in Disease Recognition. https://arxiv.org/abs/2406.18546

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