FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200551431> ?p ?o ?g. }

• W4200551431 abstract "Abstract Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments. Highlights The multi-scale feature extraction module is introduced to extract multi-scale features of images, it can extract deep context information of the lesion region and surrounding tissues to enhance the feature extraction ability of the network. The depth feature extraction module is used to focus on the local features of the lesion region from both channel and space, weaken the influence of irrelevant information, and strengthen the recognition ability of the lesion region. The feature extraction module is enhanced by the parallel structure of dense dilated convolution, and the deeper feature information is obtained without losing the image feature information to improve the classification accuracy." @default.
• W4200551431 created "2021-12-31" @default.
• W4200551431 creator A5026984704 @default.
• W4200551431 date "2021-12-07" @default.
• W4200551431 modified "2023-10-14" @default.
• W4200551431 title "A novel ResNet101 model based on dense dilated convolution for image classification" @default.
• W4200551431 cites W2097117768 @default.
• W4200551431 cites W2183341477 @default.
• W4200551431 cites W2253429366 @default.
• W4200551431 cites W2519656470 @default.
• W4200551431 cites W2526009326 @default.
• W4200551431 cites W2588463901 @default.
• W4200551431 cites W2592929672 @default.
• W4200551431 cites W2884585870 @default.
• W4200551431 cites W2904572485 @default.
• W4200551431 cites W2912801230 @default.
• W4200551431 cites W2916686134 @default.
• W4200551431 cites W2916845318 @default.
• W4200551431 cites W2922509574 @default.
• W4200551431 cites W2928165649 @default.
• W4200551431 cites W2944977302 @default.
• W4200551431 cites W2948478164 @default.
• W4200551431 cites W2951064851 @default.
• W4200551431 cites W2963125010 @default.
• W4200551431 cites W2963163009 @default.
• W4200551431 cites W2963420686 @default.
• W4200551431 cites W2963446712 @default.
• W4200551431 cites W2963918968 @default.
• W4200551431 cites W2972574169 @default.
• W4200551431 cites W2986785750 @default.
• W4200551431 cites W2988396473 @default.
• W4200551431 cites W2996895059 @default.
• W4200551431 cites W3004944598 @default.
• W4200551431 cites W3009003668 @default.
• W4200551431 cites W3023006114 @default.
• W4200551431 cites W3038204868 @default.
• W4200551431 cites W3047042937 @default.
• W4200551431 cites W3082584514 @default.
• W4200551431 cites W3088621547 @default.
• W4200551431 cites W3092076936 @default.
• W4200551431 cites W3152273321 @default.
• W4200551431 doi "https://doi.org/10.1007/s42452-021-04897-7" @default.
• W4200551431 hasPublicationYear "2021" @default.
• W4200551431 type Work @default.
• W4200551431 citedByCount "16" @default.
• W4200551431 countsByYear W42005514312022 @default.
• W4200551431 countsByYear W42005514312023 @default.
• W4200551431 crossrefType "journal-article" @default.
• W4200551431 hasAuthorship W4200551431A5026984704 @default.
• W4200551431 hasBestOaLocation W42005514311 @default.
• W4200551431 hasConcept C11413529 @default.
• W4200551431 hasConcept C115961682 @default.
• W4200551431 hasConcept C120665830 @default.
• W4200551431 hasConcept C121332964 @default.
• W4200551431 hasConcept C138885662 @default.
• W4200551431 hasConcept C151730666 @default.
• W4200551431 hasConcept C153180895 @default.
• W4200551431 hasConcept C154945302 @default.
• W4200551431 hasConcept C155512373 @default.
• W4200551431 hasConcept C192209626 @default.
• W4200551431 hasConcept C2776401178 @default.
• W4200551431 hasConcept C2778755073 @default.
• W4200551431 hasConcept C2779343474 @default.
• W4200551431 hasConcept C41008148 @default.
• W4200551431 hasConcept C41895202 @default.
• W4200551431 hasConcept C45347329 @default.
• W4200551431 hasConcept C50644808 @default.
• W4200551431 hasConcept C52622490 @default.
• W4200551431 hasConcept C62520636 @default.
• W4200551431 hasConcept C75294576 @default.
• W4200551431 hasConcept C81363708 @default.
• W4200551431 hasConcept C86803240 @default.
• W4200551431 hasConcept C99498987 @default.
• W4200551431 hasConceptScore W4200551431C11413529 @default.
• W4200551431 hasConceptScore W4200551431C115961682 @default.
• W4200551431 hasConceptScore W4200551431C120665830 @default.
• W4200551431 hasConceptScore W4200551431C121332964 @default.
• W4200551431 hasConceptScore W4200551431C138885662 @default.
• W4200551431 hasConceptScore W4200551431C151730666 @default.
• W4200551431 hasConceptScore W4200551431C153180895 @default.
• W4200551431 hasConceptScore W4200551431C154945302 @default.
• W4200551431 hasConceptScore W4200551431C155512373 @default.
• W4200551431 hasConceptScore W4200551431C192209626 @default.
• W4200551431 hasConceptScore W4200551431C2776401178 @default.
• W4200551431 hasConceptScore W4200551431C2778755073 @default.
• W4200551431 hasConceptScore W4200551431C2779343474 @default.
• W4200551431 hasConceptScore W4200551431C41008148 @default.
• W4200551431 hasConceptScore W4200551431C41895202 @default.
• W4200551431 hasConceptScore W4200551431C45347329 @default.
• W4200551431 hasConceptScore W4200551431C50644808 @default.
• W4200551431 hasConceptScore W4200551431C52622490 @default.
• W4200551431 hasConceptScore W4200551431C62520636 @default.
• W4200551431 hasConceptScore W4200551431C75294576 @default.
• W4200551431 hasConceptScore W4200551431C81363708 @default.
• W4200551431 hasConceptScore W4200551431C86803240 @default.
• W4200551431 hasConceptScore W4200551431C99498987 @default.
• W4200551431 hasIssue "1" @default.
• W4200551431 hasLocation W42005514311 @default.
• W4200551431 hasLocation W42005514312 @default.
• W4200551431 hasOpenAccess W4200551431 @default.

• next