FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226372757> ?p ?o ?g. }

• W4226372757 endingPage "11" @default.
• W4226372757 startingPage "1" @default.
• W4226372757 abstract "Electrical impedance tomography (EIT) is an emerging technique for medical imaging. According to reconstructed images, the human disease can be identified. Note that the reconstruction of EIT is an ill-posed inverse problem. To solve this problem, various sensitivity-based methods have been proposed. Nevertheless, a sensitivity matrix is affected by a number of factors. Therefore, classification reflected by images is sometimes not very reliable. In this work, a novel method based on a residual neural network is proposed for stroke classification. It is realized by directly processing measured difference data from EIT. After training of the residual neural network, the proposed method is quantitatively evaluated when noise is exerted, shape deviation occurs, and conductivity varies in three different layers. Also, impacts of contact impedance and small-sized inclusion are studied. Furthermore, phantom experiments are conducted. Compared with fully connected neural networks and shallow convolution neural networks, the results demonstrate that the proposed method has a better performance in classification for various cases. This article offers an effective alternative for stroke classification in EIT." @default.
• W4226372757 created "2022-05-05" @default.
• W4226372757 creator A5013551265 @default.
• W4226372757 creator A5021010198 @default.
• W4226372757 creator A5025428519 @default.
• W4226372757 creator A5036726873 @default.
• W4226372757 creator A5056640178 @default.
• W4226372757 creator A5082493498 @default.
• W4226372757 date "2022-01-01" @default.
• W4226372757 modified "2023-10-16" @default.
• W4226372757 title "Residual Convolutional Neural Network-Based Stroke Classification With Electrical Impedance Tomography" @default.
• W4226372757 cites W1529548832 @default.
• W4226372757 cites W1859595342 @default.
• W4226372757 cites W1982196474 @default.
• W4226372757 cites W2024065953 @default.
• W4226372757 cites W2094141120 @default.
• W4226372757 cites W2121514123 @default.
• W4226372757 cites W2128648870 @default.
• W4226372757 cites W2134934663 @default.
• W4226372757 cites W2191307768 @default.
• W4226372757 cites W2284794734 @default.
• W4226372757 cites W2342572800 @default.
• W4226372757 cites W2396597812 @default.
• W4226372757 cites W2735205850 @default.
• W4226372757 cites W2787657543 @default.
• W4226372757 cites W2803200748 @default.
• W4226372757 cites W2858985345 @default.
• W4226372757 cites W2883645217 @default.
• W4226372757 cites W2945952022 @default.
• W4226372757 cites W2955007489 @default.
• W4226372757 cites W2962092236 @default.
• W4226372757 cites W2964293140 @default.
• W4226372757 cites W2999516274 @default.
• W4226372757 cites W3004288566 @default.
• W4226372757 cites W3009122075 @default.
• W4226372757 cites W3010369603 @default.
• W4226372757 cites W3045018947 @default.
• W4226372757 cites W3046520725 @default.
• W4226372757 cites W3084741591 @default.
• W4226372757 cites W3087721441 @default.
• W4226372757 cites W3104579665 @default.
• W4226372757 cites W3119709225 @default.
• W4226372757 cites W3136754943 @default.
• W4226372757 cites W3143742954 @default.
• W4226372757 cites W3159022280 @default.
• W4226372757 cites W3173725052 @default.
• W4226372757 cites W3175346817 @default.
• W4226372757 cites W3175610975 @default.
• W4226372757 cites W3197366523 @default.
• W4226372757 cites W3200077256 @default.
• W4226372757 cites W359288994 @default.
• W4226372757 cites W4210973470 @default.
• W4226372757 cites W4226421883 @default.
• W4226372757 doi "https://doi.org/10.1109/tim.2022.3165786" @default.
• W4226372757 hasPublicationYear "2022" @default.
• W4226372757 type Work @default.
• W4226372757 citedByCount "4" @default.
• W4226372757 countsByYear W42263727572022 @default.
• W4226372757 countsByYear W42263727572023 @default.
• W4226372757 crossrefType "journal-article" @default.
• W4226372757 hasAuthorship W4226372757A5013551265 @default.
• W4226372757 hasAuthorship W4226372757A5021010198 @default.
• W4226372757 hasAuthorship W4226372757A5025428519 @default.
• W4226372757 hasAuthorship W4226372757A5036726873 @default.
• W4226372757 hasAuthorship W4226372757A5056640178 @default.
• W4226372757 hasAuthorship W4226372757A5082493498 @default.
• W4226372757 hasConcept C104293457 @default.
• W4226372757 hasConcept C11413529 @default.
• W4226372757 hasConcept C115961682 @default.
• W4226372757 hasConcept C120665830 @default.
• W4226372757 hasConcept C121332964 @default.
• W4226372757 hasConcept C127413603 @default.
• W4226372757 hasConcept C134306372 @default.
• W4226372757 hasConcept C135252773 @default.
• W4226372757 hasConcept C141379421 @default.
• W4226372757 hasConcept C153180895 @default.
• W4226372757 hasConcept C154945302 @default.
• W4226372757 hasConcept C155175808 @default.
• W4226372757 hasConcept C155512373 @default.
• W4226372757 hasConcept C163716698 @default.
• W4226372757 hasConcept C17829176 @default.
• W4226372757 hasConcept C21200559 @default.
• W4226372757 hasConcept C24326235 @default.
• W4226372757 hasConcept C2780645631 @default.
• W4226372757 hasConcept C33923547 @default.
• W4226372757 hasConcept C41008148 @default.
• W4226372757 hasConcept C45347329 @default.
• W4226372757 hasConcept C50644808 @default.
• W4226372757 hasConcept C62520636 @default.
• W4226372757 hasConcept C81363708 @default.
• W4226372757 hasConcept C97355855 @default.
• W4226372757 hasConcept C99498987 @default.
• W4226372757 hasConceptScore W4226372757C104293457 @default.
• W4226372757 hasConceptScore W4226372757C11413529 @default.
• W4226372757 hasConceptScore W4226372757C115961682 @default.
• W4226372757 hasConceptScore W4226372757C120665830 @default.
• W4226372757 hasConceptScore W4226372757C121332964 @default.
• W4226372757 hasConceptScore W4226372757C127413603 @default.

• next