Application of Multimodal Fusion Deep Learning Model in Disease Recognition

TL;DR

This paper presents a novel multimodal fusion deep learning approach that combines CNNs, RNNs, and transformers to improve disease recognition accuracy by integrating diverse data sources.

Contribution

It introduces an innovative fusion strategy that optimally combines features from multiple modalities, surpassing traditional single-modal recognition methods.

Findings

Multimodal fusion model outperforms single-modal methods in accuracy.

Significant improvement in diagnostic metrics across datasets.

Effective integration of image, temporal, and structured data.

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.