Summary of Application Of Multimodal Fusion Deep Learning Model in Disease Recognition, by Xiaoyi Liu et al.
Application of Multimodal Fusion Deep Learning Model in Disease Recognition
by Xiaoyi Liu, Hongjie Qiu, Muqing Li, Zhou Yu, Yutian Yang, Yafeng Yan
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers present a novel deep learning approach that combines multiple data sources to improve disease recognition accuracy. By applying convolutional neural networks (CNN), recurrent neural networks (RNN), and transformers to image-based, temporal, and structured data, the method extracts advanced features. The fusion strategy component determines the optimal fusion mode for each disease recognition task. Experimental results show significant advantages of the multimodal fusion model over traditional single-mode recognition methods across various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how combining different types of data can help doctors better diagnose diseases. It uses special kinds of computer programs called deep learning models to take in information from images, sounds, and other sources. The program then combines this information to make a more accurate diagnosis. This is important because sometimes doctors don’t have enough information to make an accurate diagnosis. By combining different types of data, the program can fill in the gaps and give doctors a better idea of what’s going on. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Rnn
