Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier

Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively i...

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Bibliographic Details
Main Authors: Yingjian Yang, Wei Li, Yingwei Guo, Nanrong Zeng, Shicong Wang, Ziran Chen, Yang Liu, Huai Chen, Wenxin Duan, Xian Li, Wei Zhao, Rongchang Chen, Yan Kang
Format: Article
Language:English
Published: AIMS Press 2022-05-01
Series:Mathematical Biosciences and Engineering
Subjects:
radiomics
copd stage (gold)
classification
feature combination
lasso
convolutional neural networks (cnn)
machine learning (ml)
chest hrct images
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022366?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2022366?viewType=HTML

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