Research and implementation of multi-disease diagnosis on chest X-ray based on vision transformer.

ABSTRACT:

Background

Disease diagnosis in chest X-ray images has predominantly relied on convolutional neural networks (CNNs). However, Vision Transformer (ViT) offers several advantages over CNNs, as it excels at capturing long-term dependencies, exploring correlations, and extracting features with richer semantic information.

Methods

We adapted ViT for chest X-ray image analysis by making the following three key improvements: (I) employing a sliding window approach in the image sequence feature extraction module to divide the input image into blocks to identify small and difficult-to-detect lesion areas; (II) introducing an attention region selection module in the encoder layer of the ViT model to enhance the model's ability to focus on relevant regions; and (III) constructing a par

SUBMITTER: Huang L 

PROVIDER: S-EPMC10963853 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature