A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images

•A cascade framework is proposed for automatic lung segmentation in CXRs.•An adaptive closed polyline searching method is used to obtain data sequence.•An improved machine learning model is proposed to express a mathematical model.•The explainability-guided mathematical model is used to denote lung...

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Veröffentlicht in:	Expert systems with applications Jg. 198; S. 116873
Hauptverfasser:	Peng, Tao, Gu, Yidong, Ye, Zhenyu, Cheng, Xiuxiu, Wang, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 15.07.2022 Elsevier BV
Schlagworte:	Algorithms Automatic lung segmentation Chest Chest radiographs Datasets Deep learning Explainability-guided mathematical model Fractional-order backpropagation learning algorithm Image segmentation Improved adaptive closed polyline searching algorithm Lungs Machine learning Mask-RCNN Mathematical models Multi-site dataset Performance evaluation Principal curve Sensitivity Similarity coefficient method X-rays Improved adaptive closed polyline searching algorithm Multi-site dataset Mask-RCNN Chest radiographs Fractional-order backpropagation learning algorithm Automatic lung segmentation Principal curve Explainability-guided mathematical model
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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