Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.

ABSTRACT:

Background

The accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging. This study adopts a novel approach, using computed tomography (CT) radiomics to analyze the quantitative features in CT images to reveal the characteristics of lung nodules, and then employs diversity-weighted ensemble learning to enhance the accuracy of classification by integrating the predictive results of multiple mod

SUBMITTER: Tang G 

PROVIDER: S-EPMC11651971 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature