mlsanityData to 7 Oct 2026

Application of Multimodal Fusion Deep Learning Model in Disease Recognition

arXiv 2406.18546 ↗First posted 22 May 2024cs.CV cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

15 of 29 references.

References · 15

  1. 2023A Survey on Multimodal Large Language Modelscited by 898
  2. 2023DataComp: In search of the next generation of multimodal datasetscited by 635
  3. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  4. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  5. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  6. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
  7. 2024Exploring Diverse Methods in Visual Question Answeringcited by 45
  8. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  9. 2024Regional Style and Color Transfercited by 30
  10. 20233D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimationcited by 28
  11. 2022Visualizing and Understanding Patch Interactions in Vision Transformercited by 26
  12. 2021An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Datasetcited by 18
3 more
  1. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  2. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15
  3. 2024DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence eracited by 9

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Cited by year

Citing papers per year: 2024: 12.

12 papers.

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  3. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  4. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  5. 2024Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integratedcited by 8
  6. 2024Text classification optimization algorithm based on graph neural networkcited by 8
  7. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  8. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  9. 2024Comprehensive Overview of Artificial Intelligence Applications in Modern Industriescited by 6
  10. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  11. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5
  12. 2024Enhanced Credit Score Prediction Using Ensemble Deep Learning Modelcited by 2