2022
DOI: 10.1111/coin.12531
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A novel lung nodule accurate detection of computerized tomography images based on convolutional neural network and probability graph model

Abstract: Precisely detecting lung nodules from original computerized tomography (CT) images is a critical technology in the earlier screening of lung cancer. Therefore, the domain of accurate detection has gradually attracted the attention of researchers. However, due to the complex characteristics of pulmonary nodules and the limita-1728

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Cited by 2 publications
(1 citation statement)
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“…The various training models and methodologies were tested using six distinct methods. By creatively integrating a probability graph model in the candidate detection and false‐positive reduction phase, Xia et al 31 suggested an efficient and reliable detection network to precisely detect lung nodules. We offer two efficient probability graph algorithms that assess multiscale information and continuous slice motion information to increase performance, in contrast to prior efforts that used complex 3‐dimensional picture information to reduce false positives.…”
Section: Related Workmentioning
confidence: 99%
“…The various training models and methodologies were tested using six distinct methods. By creatively integrating a probability graph model in the candidate detection and false‐positive reduction phase, Xia et al 31 suggested an efficient and reliable detection network to precisely detect lung nodules. We offer two efficient probability graph algorithms that assess multiscale information and continuous slice motion information to increase performance, in contrast to prior efforts that used complex 3‐dimensional picture information to reduce false positives.…”
Section: Related Workmentioning
confidence: 99%