FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3212760103> ?p ?o ?g. }

• W3212760103 abstract "Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules.3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907.The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules." @default.
• W3212760103 created "2021-11-22" @default.
• W3212760103 creator A5029714740 @default.
• W3212760103 creator A5032865037 @default.
• W3212760103 creator A5067855146 @default.
• W3212760103 creator A5067939471 @default.
• W3212760103 creator A5068095614 @default.
• W3212760103 creator A5087530415 @default.
• W3212760103 date "2021-11-01" @default.
• W3212760103 modified "2023-10-09" @default.
• W3212760103 title "Design of lung nodules segmentation and recognition algorithm based on deep learning" @default.
• W3212760103 cites W1901129140 @default.
• W3212760103 cites W1986649315 @default.
• W3212760103 cites W1987805244 @default.
• W3212760103 cites W1992122542 @default.
• W3212760103 cites W2009169167 @default.
• W3212760103 cites W2129597285 @default.
• W3212760103 cites W2132014319 @default.
• W3212760103 cites W2148076415 @default.
• W3212760103 cites W2154720005 @default.
• W3212760103 cites W2167803594 @default.
• W3212760103 cites W2194775991 @default.
• W3212760103 cites W2395611524 @default.
• W3212760103 cites W2514793708 @default.
• W3212760103 cites W2551780400 @default.
• W3212760103 cites W2560023338 @default.
• W3212760103 cites W2584017349 @default.
• W3212760103 cites W2594318146 @default.
• W3212760103 cites W2604463720 @default.
• W3212760103 cites W2626997132 @default.
• W3212760103 cites W2732063980 @default.
• W3212760103 cites W2753801833 @default.
• W3212760103 cites W2769848455 @default.
• W3212760103 cites W2783591863 @default.
• W3212760103 cites W2899912692 @default.
• W3212760103 cites W2911188335 @default.
• W3212760103 cites W2911634634 @default.
• W3212760103 cites W2940711273 @default.
• W3212760103 cites W2962914239 @default.
• W3212760103 cites W2963777800 @default.
• W3212760103 cites W2970016848 @default.
• W3212760103 cites W2991714820 @default.
• W3212760103 cites W3034595523 @default.
• W3212760103 cites W3045605181 @default.
• W3212760103 cites W3101394124 @default.
• W3212760103 cites W3105636206 @default.
• W3212760103 cites W948663339 @default.
• W3212760103 doi "https://doi.org/10.1186/s12859-021-04234-0" @default.
• W3212760103 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8576909" @default.
• W3212760103 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34749636" @default.
• W3212760103 hasPublicationYear "2021" @default.
• W3212760103 type Work @default.
• W3212760103 sameAs 3212760103 @default.
• W3212760103 citedByCount "12" @default.
• W3212760103 countsByYear W32127601032022 @default.
• W3212760103 countsByYear W32127601032023 @default.
• W3212760103 crossrefType "journal-article" @default.
• W3212760103 hasAuthorship W3212760103A5029714740 @default.
• W3212760103 hasAuthorship W3212760103A5032865037 @default.
• W3212760103 hasAuthorship W3212760103A5067855146 @default.
• W3212760103 hasAuthorship W3212760103A5067939471 @default.
• W3212760103 hasAuthorship W3212760103A5068095614 @default.
• W3212760103 hasAuthorship W3212760103A5087530415 @default.
• W3212760103 hasBestOaLocation W32127601031 @default.
• W3212760103 hasConcept C105795698 @default.
• W3212760103 hasConcept C11413529 @default.
• W3212760103 hasConcept C124504099 @default.
• W3212760103 hasConcept C153180895 @default.
• W3212760103 hasConcept C154945302 @default.
• W3212760103 hasConcept C155512373 @default.
• W3212760103 hasConcept C163892561 @default.
• W3212760103 hasConcept C22029948 @default.
• W3212760103 hasConcept C33923547 @default.
• W3212760103 hasConcept C41008148 @default.
• W3212760103 hasConcept C89600930 @default.
• W3212760103 hasConceptScore W3212760103C105795698 @default.
• W3212760103 hasConceptScore W3212760103C11413529 @default.
• W3212760103 hasConceptScore W3212760103C124504099 @default.
• W3212760103 hasConceptScore W3212760103C153180895 @default.
• W3212760103 hasConceptScore W3212760103C154945302 @default.
• W3212760103 hasConceptScore W3212760103C155512373 @default.
• W3212760103 hasConceptScore W3212760103C163892561 @default.
• W3212760103 hasConceptScore W3212760103C22029948 @default.
• W3212760103 hasConceptScore W3212760103C33923547 @default.
• W3212760103 hasConceptScore W3212760103C41008148 @default.
• W3212760103 hasConceptScore W3212760103C89600930 @default.
• W3212760103 hasFunder F4320321543 @default.
• W3212760103 hasIssue "S5" @default.
• W3212760103 hasLocation W32127601031 @default.
• W3212760103 hasLocation W32127601032 @default.
• W3212760103 hasLocation W32127601033 @default.
• W3212760103 hasLocation W32127601034 @default.
• W3212760103 hasLocation W32127601035 @default.
• W3212760103 hasOpenAccess W3212760103 @default.
• W3212760103 hasPrimaryLocation W32127601031 @default.
• W3212760103 hasRelatedWork W2239377373 @default.
• W3212760103 hasRelatedWork W2397398970 @default.
• W3212760103 hasRelatedWork W2949961661 @default.
• W3212760103 hasRelatedWork W2973136608 @default.
• W3212760103 hasRelatedWork W2983365766 @default.

• next