Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis

Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis

Abstract Purpose This paper aims to develop a successful deep learning model with data augmentation technique to discover the clinical uniqueness of chest X-ray imaging features of coal workers' pneumoconiosis (CWP). Patients and methods We enrolled 149 CWP patients and 68 dust-exposure workers...

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Bibliographic Details
Main Authors: Hantian Dong, Biaokai Zhu, Xinri Zhang, Xiaomei Kong
Format: Article
Language:English
Published: BMC 2022-07-01
Series:BMC Pulmonary Medicine
Subjects:
Coal workers' pneumoconiosis classification
Chest X-ray
Deep learning
ShuffleNet
ECA-Net
Data augmentation
Online Access:https://doi.org/10.1186/s12890-022-02068-x

Internet

https://doi.org/10.1186/s12890-022-02068-x

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