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.
@article{arxiv.2406.18546,
title = {Application of Multimodal Fusion Deep Learning Model in Disease Recognition},
author = {Xiaoyi Liu and Hongjie Qiu and Muqing Li and Zhou Yu and Yutian Yang and Yafeng Yan},
journal= {arXiv preprint arXiv:2406.18546},
year = {2024}
}