Pulmonary Lesion Classification Framework Using the Weighted Ensemble Classification with Random Forest and CNN Models for EBUS Images

Pulmonary Lesion Classification Framework Using the Weighted Ensemble Classification with Random Forest and CNN Models for EBUS Images

Lung cancer is a deadly disease with a high mortality rate. Endobronchial ultrasonography (EBUS) is one of the methods for detecting pulmonary lesions. Computer-aided diagnosis of pulmonary lesions from images can help radiologists to classify lesions; however, most of the existing methods need a la...

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Bibliographic Details
Main Authors: Banphatree Khomkham, Rajalida Lipikorn
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Diagnostics
Subjects:
pulmonary lesion
endobronchial ultrasonography images (EBUS)
convolutional neural network (CNN)
radiomics features
random forest
gray-level co-occurrence matrix (GLCM)
Online Access:https://www.mdpi.com/2075-4418/12/7/1552

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