Features processing for random forest optimization in lung nodule localization
作者：
Highlights：
• Random Forest is trained with features extracted from pixels of lung CT images.
• Used segmentation as auxiliary step to improve results of region properties feature.
• Reduced false positive rate to reach lung nodule localization with optimized forest.
• Accuracy improved when compare results to our previous model and other researches.
• Used 214 cases from a standard dataset with total 2124 CT lung slices.
摘要
•Random Forest is trained with features extracted from pixels of lung CT images.•Used segmentation as auxiliary step to improve results of region properties feature.•Reduced false positive rate to reach lung nodule localization with optimized forest.•Accuracy improved when compare results to our previous model and other researches.•Used 214 cases from a standard dataset with total 2124 CT lung slices.
论文关键词：Lung nodule localization,Computed Tomography,Automatic detection,Random forest,Lung features,Feature processing
论文评审过程：Received 31 May 2020, Revised 4 December 2021, Accepted 30 December 2021, Available online 10 January 2022, Version of Record 13 January 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116489
