Work

Application of Multimodal Fusion Deep Learning Model in Disease Recognition

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE)

DOI: 10.1109/icsece61636.2024.10729504

Authors: Xiaoyi Liu, Hongjie Qiu, Muqing Li, Zhou Yu, Yutian Yang, Yafeng Yan

Institutions: Arizona State University, University of Washington, University of California San Diego, University of Illinois Chicago, University of California, Davis, Stevens Institute of Technology

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4403864711

FWCI: 6.314

Cites: 25

Cited by: 30

Related to: 10