FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200175037> ?p ?o ?g. }

• W4200175037 endingPage "103398" @default.
• W4200175037 startingPage "103398" @default.
• W4200175037 abstract "To detect and diagnosis the lungs related diseases, a Chest X-Ray (CXR) is the major tool used by the physician. Automated organ segmentation contributes to a crucial part of computer-aided detection (CAD) and diagnosis of diseases from CXRs as they enhance the accuracy in detecting, and also help in reducing the burden raised due to manual diagnosis from radiologists and medical practitioners. In this paper, an efficient automatic CAD system is proposed to detect the boundaries using a deep convolutional neural network (DCNN) model. The DCNN is trained in an end-to-end setting to facilitate fully automatic lung segmentation from anteroposterior or posteroanterior view CXRs. It learns to predict binary masks for a given CXR, by learning to discriminate regions of organ parenchyma from regions of no organ parenchyma. The proposed model’s architecture makes use of residual connections in all the concurrent up-sampling paths from each encoder block at every level, thus facilitating collective learning within blocks through inter-sharing of all high-dimensional feature maps. To generalize the proposed model to CXRs from all data distributions, image preprocessing techniques such as Top-Hat Bottom-Hat Transform and Contrast Limited Adaptive Histogram Equalization are employed. The proposed model is trained and tested using the JSRT, NLM-MC and Shenzhen Hospital datasets. The proposed method achieved a Dice Similarity Coefficient of 0.982 ± 0.018 and a Jaccard Similarity Coefficient of 0.967 ± 0.015. The implementation results demonstrated that the proposed method has surpassed the existing methods and our model is relatively lightweight and can be easily implemented on standard GPUs." @default.
• W4200175037 created "2021-12-31" @default.
• W4200175037 creator A5001597665 @default.
• W4200175037 creator A5058031212 @default.
• W4200175037 creator A5062882178 @default.
• W4200175037 creator A5087151654 @default.
• W4200175037 date "2022-03-01" @default.
• W4200175037 modified "2023-09-25" @default.
• W4200175037 title "Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays" @default.
• W4200175037 cites W1994062553 @default.
• W4200175037 cites W1997458394 @default.
• W4200175037 cites W2010727394 @default.
• W4200175037 cites W2024798729 @default.
• W4200175037 cites W2194775991 @default.
• W4200175037 cites W2383601426 @default.
• W4200175037 cites W2561981131 @default.
• W4200175037 cites W2621285644 @default.
• W4200175037 cites W2791366370 @default.
• W4200175037 cites W2793954249 @default.
• W4200175037 cites W2794103425 @default.
• W4200175037 cites W2801827608 @default.
• W4200175037 cites W2841732318 @default.
• W4200175037 cites W2884436604 @default.
• W4200175037 cites W2885112059 @default.
• W4200175037 cites W2913081511 @default.
• W4200175037 cites W2933170392 @default.
• W4200175037 cites W2947263797 @default.
• W4200175037 cites W2952855260 @default.
• W4200175037 cites W2963881378 @default.
• W4200175037 cites W3015390407 @default.
• W4200175037 cites W3018407595 @default.
• W4200175037 cites W3035899512 @default.
• W4200175037 cites W3045156229 @default.
• W4200175037 cites W3086675810 @default.
• W4200175037 cites W3093162969 @default.
• W4200175037 cites W3112139896 @default.
• W4200175037 cites W3119260780 @default.
• W4200175037 cites W3136424010 @default.
• W4200175037 doi "https://doi.org/10.1016/j.bspc.2021.103398" @default.
• W4200175037 hasPublicationYear "2022" @default.
• W4200175037 type Work @default.
• W4200175037 citedByCount "11" @default.
• W4200175037 countsByYear W42001750372022 @default.
• W4200175037 countsByYear W42001750372023 @default.
• W4200175037 crossrefType "journal-article" @default.
• W4200175037 hasAuthorship W4200175037A5001597665 @default.
• W4200175037 hasAuthorship W4200175037A5058031212 @default.
• W4200175037 hasAuthorship W4200175037A5062882178 @default.
• W4200175037 hasAuthorship W4200175037A5087151654 @default.
• W4200175037 hasConcept C103278499 @default.
• W4200175037 hasConcept C108583219 @default.
• W4200175037 hasConcept C115961682 @default.
• W4200175037 hasConcept C124504099 @default.
• W4200175037 hasConcept C127413603 @default.
• W4200175037 hasConcept C138885662 @default.
• W4200175037 hasConcept C153180895 @default.
• W4200175037 hasConcept C154945302 @default.
• W4200175037 hasConcept C163892561 @default.
• W4200175037 hasConcept C194789388 @default.
• W4200175037 hasConcept C199639397 @default.
• W4200175037 hasConcept C203519979 @default.
• W4200175037 hasConcept C2776401178 @default.
• W4200175037 hasConcept C2779549770 @default.
• W4200175037 hasConcept C31972630 @default.
• W4200175037 hasConcept C34736171 @default.
• W4200175037 hasConcept C41008148 @default.
• W4200175037 hasConcept C41895202 @default.
• W4200175037 hasConcept C81363708 @default.
• W4200175037 hasConcept C89600930 @default.
• W4200175037 hasConceptScore W4200175037C103278499 @default.
• W4200175037 hasConceptScore W4200175037C108583219 @default.
• W4200175037 hasConceptScore W4200175037C115961682 @default.
• W4200175037 hasConceptScore W4200175037C124504099 @default.
• W4200175037 hasConceptScore W4200175037C127413603 @default.
• W4200175037 hasConceptScore W4200175037C138885662 @default.
• W4200175037 hasConceptScore W4200175037C153180895 @default.
• W4200175037 hasConceptScore W4200175037C154945302 @default.
• W4200175037 hasConceptScore W4200175037C163892561 @default.
• W4200175037 hasConceptScore W4200175037C194789388 @default.
• W4200175037 hasConceptScore W4200175037C199639397 @default.
• W4200175037 hasConceptScore W4200175037C203519979 @default.
• W4200175037 hasConceptScore W4200175037C2776401178 @default.
• W4200175037 hasConceptScore W4200175037C2779549770 @default.
• W4200175037 hasConceptScore W4200175037C31972630 @default.
• W4200175037 hasConceptScore W4200175037C34736171 @default.
• W4200175037 hasConceptScore W4200175037C41008148 @default.
• W4200175037 hasConceptScore W4200175037C41895202 @default.
• W4200175037 hasConceptScore W4200175037C81363708 @default.
• W4200175037 hasConceptScore W4200175037C89600930 @default.
• W4200175037 hasLocation W42001750371 @default.
• W4200175037 hasOpenAccess W4200175037 @default.
• W4200175037 hasPrimaryLocation W42001750371 @default.
• W4200175037 hasRelatedWork W2809504579 @default.
• W4200175037 hasRelatedWork W2969790209 @default.
• W4200175037 hasRelatedWork W3118494652 @default.
• W4200175037 hasRelatedWork W3135324209 @default.
• W4200175037 hasRelatedWork W4200528772 @default.
• W4200175037 hasRelatedWork W4213065901 @default.

• next