Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

ABSTRACT:

Background

Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.

Methods

In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model.

SUBMITTER: Liu C 

PROVIDER: S-EPMC11221180 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature