FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285362271> ?p ?o ?g. }

• W4285362271 endingPage "88" @default.
• W4285362271 startingPage "75" @default.
• W4285362271 abstract "This research proposes a novel self-adaptive convolutional neural network (Adap-Net) model for lung nodule diagnosis on 3D computed tomography (CT) images. Lung cancer is one of the most common cancers with a high mortality rate. Therefore, there is an urgent need to diagnose lung nodules to improve the survival rate, which is challenging because of the nodule heterogeneity and the lack of annotated lung nodule images. Prevailing research for lung nodule diagnosis usually ignores the nodule heterogeneity problem and enlarges the model complexity that degrades the lung nodule diagnosis performance given limited annotated training samples. To overcome the challenges, a transverse layer pooling (TLP) algorithm is proposed and the spatial pyramid pooling (SPP) scheme is integrated, which makes it possible to adaptively extract equal-dimensional feature representations from arbitrary-sized 3D lung nodule images. Meanwhile, the TLP algorithm introduces a layer compression architecture that dramatically reduces the model complexity. Moreover, K-means clustering is adopted to assign appropriate input image sizes for each lung nodule, allowing the mini-batch-based model training. The proposed Adap-Net is comprehensively evaluated and compared to other deep learning (DL) models using 3D CT images from a public dataset. Experimental results show that the proposed Adap-Net model improves the lung nodule diagnosis accuracy up to 12.12% with less than 10% of parameters that are involved in other DL models. In practice, the proposed Adap-Net model can offer complementary opinions in computer-aided diagnosis (CAD) systems as a supportive tool for radiologists and physicians in the medical image interpretation, analysis, and diagnosis process." @default.
• W4285362271 created "2022-07-14" @default.
• W4285362271 creator A5002827424 @default.
• W4285362271 creator A5019858998 @default.
• W4285362271 date "2021-09-23" @default.
• W4285362271 modified "2023-09-24" @default.
• W4285362271 title "A novel self-adaptive convolutional neural network model using spatial pyramid pooling for 3D lung nodule computer-aided diagnosis" @default.
• W4285362271 cites W1898227994 @default.
• W4285362271 cites W1974165720 @default.
• W4285362271 cites W1986649315 @default.
• W4285362271 cites W2005134193 @default.
• W4285362271 cites W2011430131 @default.
• W4285362271 cites W2042318131 @default.
• W4285362271 cites W2044097773 @default.
• W4285362271 cites W2067123389 @default.
• W4285362271 cites W2069914810 @default.
• W4285362271 cites W2078014989 @default.
• W4285362271 cites W2102634410 @default.
• W4285362271 cites W2106345178 @default.
• W4285362271 cites W2109255472 @default.
• W4285362271 cites W2110062437 @default.
• W4285362271 cites W2153601733 @default.
• W4285362271 cites W2164984476 @default.
• W4285362271 cites W2237167366 @default.
• W4285362271 cites W2322371438 @default.
• W4285362271 cites W2323929895 @default.
• W4285362271 cites W2325627219 @default.
• W4285362271 cites W2394599079 @default.
• W4285362271 cites W2427013826 @default.
• W4285362271 cites W2531409750 @default.
• W4285362271 cites W2547112596 @default.
• W4285362271 cites W2613475099 @default.
• W4285362271 cites W2743008510 @default.
• W4285362271 cites W2760946358 @default.
• W4285362271 cites W2762615350 @default.
• W4285362271 cites W2773381949 @default.
• W4285362271 cites W2777420618 @default.
• W4285362271 cites W2784119176 @default.
• W4285362271 cites W2790539339 @default.
• W4285362271 cites W2793409683 @default.
• W4285362271 cites W2793661135 @default.
• W4285362271 cites W2794187429 @default.
• W4285362271 cites W2897755679 @default.
• W4285362271 cites W2907066824 @default.
• W4285362271 cites W2911188335 @default.
• W4285362271 cites W2921947583 @default.
• W4285362271 cites W2937002248 @default.
• W4285362271 cites W2962852641 @default.
• W4285362271 cites W2963488165 @default.
• W4285362271 cites W2963777800 @default.
• W4285362271 cites W2964780499 @default.
• W4285362271 cites W3007131888 @default.
• W4285362271 cites W3028552847 @default.
• W4285362271 cites W3046510185 @default.
• W4285362271 cites W3090189846 @default.
• W4285362271 cites W3091908957 @default.
• W4285362271 cites W948663339 @default.
• W4285362271 doi "https://doi.org/10.1080/24725579.2021.1953638" @default.
• W4285362271 hasPublicationYear "2021" @default.
• W4285362271 type Work @default.
• W4285362271 citedByCount "5" @default.
• W4285362271 countsByYear W42853622712022 @default.
• W4285362271 countsByYear W42853622712023 @default.
• W4285362271 crossrefType "journal-article" @default.
• W4285362271 hasAuthorship W4285362271A5002827424 @default.
• W4285362271 hasAuthorship W4285362271A5019858998 @default.
• W4285362271 hasConcept C108583219 @default.
• W4285362271 hasConcept C138885662 @default.
• W4285362271 hasConcept C142575187 @default.
• W4285362271 hasConcept C151730666 @default.
• W4285362271 hasConcept C153180895 @default.
• W4285362271 hasConcept C154945302 @default.
• W4285362271 hasConcept C2524010 @default.
• W4285362271 hasConcept C2776401178 @default.
• W4285362271 hasConcept C2776731575 @default.
• W4285362271 hasConcept C2779549770 @default.
• W4285362271 hasConcept C33923547 @default.
• W4285362271 hasConcept C41008148 @default.
• W4285362271 hasConcept C41895202 @default.
• W4285362271 hasConcept C70437156 @default.
• W4285362271 hasConcept C81363708 @default.
• W4285362271 hasConcept C86803240 @default.
• W4285362271 hasConceptScore W4285362271C108583219 @default.
• W4285362271 hasConceptScore W4285362271C138885662 @default.
• W4285362271 hasConceptScore W4285362271C142575187 @default.
• W4285362271 hasConceptScore W4285362271C151730666 @default.
• W4285362271 hasConceptScore W4285362271C153180895 @default.
• W4285362271 hasConceptScore W4285362271C154945302 @default.
• W4285362271 hasConceptScore W4285362271C2524010 @default.
• W4285362271 hasConceptScore W4285362271C2776401178 @default.
• W4285362271 hasConceptScore W4285362271C2776731575 @default.
• W4285362271 hasConceptScore W4285362271C2779549770 @default.
• W4285362271 hasConceptScore W4285362271C33923547 @default.
• W4285362271 hasConceptScore W4285362271C41008148 @default.
• W4285362271 hasConceptScore W4285362271C41895202 @default.
• W4285362271 hasConceptScore W4285362271C70437156 @default.
• W4285362271 hasConceptScore W4285362271C81363708 @default.
• W4285362271 hasConceptScore W4285362271C86803240 @default.

• next