ILDIM-MFAM: interstitial lung disease identification model with multi-modal fusion attention mechanism.

ABSTRACT: This study aims to address the potential and challenges of multimodal medical information in the diagnosis of interstitial lung disease (ILD) by developing an ILD identification model (ILDIM) based on the multimodal fusion attention mechanism (MFAM) to improve the accuracy and reliability of ILD. Large-scale multimodal medical information data, including chest CT image slices, physiological indicator time series data, and patient history text information were collected. These data are professionally cleaned and normalized to ensure data quality and consistency. Convolutional Neural Network (CNN) is used to extract CT image features, Bidirectional Long Short-Term Memory Network (Bi-LSTM) model is used to learn temporal physiological metrics data under long-term dependency, and Self-Attentio

SUBMITTER: Zhong B 

PROVIDER: S-EPMC11609931 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature