FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377019632> ?p ?o ?g. }

• W4377019632 endingPage "366" @default.
• W4377019632 startingPage "353" @default.
• W4377019632 abstract "Numerous studies have explored different techniques for segmenting breast cancer images, in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted attention. However, due to their down-and-out pursuance, the existing approaches like FCN (Fully Convolutional Network), PSPNet (Pyramid Scene Parsing Network), U-Net, and SegNet still required improvement for offering better semantic segmentation while identifying breast cancer. In this paper, the newly proposed breast cancer tumor segmentation method consists of four steps pre-processing, augmentation, segmenting image using multi-scale convolution and multi- attention mechanisms respectively. The proposed method utilizes the ResNet (Residual Network) backbone network with multi-scale convolution for feature map prediction. Also, the effectiveness of the multi-channel attention module with a pyramid dilated nodule is employed for semantic segmentation. Gated axial, position, and channel attention are combined to create a multi-channel attention mechanism. Additionally, War Search Optimization (WSO) algorithm is being utilized to enhance the accuracy of the segmented images. Experimentations are conducted on two datasets, viz., Breast Cancer Cell Segmentation Database and Breast Cancer Semantic Segmentation (BCSS) Database, with different existing networks. The effectiveness of the network is evaluated based on various criteria in terms of precision, accuracy, recall, mIoU (mean Intersection of Union), IoU (Intersection of Union), etc." @default.
• W4377019632 created "2023-05-19" @default.
• W4377019632 creator A5015354496 @default.
• W4377019632 creator A5054276800 @default.
• W4377019632 creator A5087584441 @default.
• W4377019632 date "2023-05-04" @default.
• W4377019632 modified "2023-09-26" @default.
• W4377019632 title "Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization" @default.
• W4377019632 cites W2566352549 @default.
• W4377019632 cites W2900144270 @default.
• W4377019632 cites W2963881378 @default.
• W4377019632 cites W2965845428 @default.
• W4377019632 cites W2980935009 @default.
• W4377019632 cites W3011941780 @default.
• W4377019632 cites W3013475455 @default.
• W4377019632 cites W3034436104 @default.
• W4377019632 cites W3044261867 @default.
• W4377019632 cites W3049209383 @default.
• W4377019632 cites W3083194557 @default.
• W4377019632 cites W3083699157 @default.
• W4377019632 cites W3088177481 @default.
• W4377019632 cites W3089090082 @default.
• W4377019632 cites W3092412781 @default.
• W4377019632 cites W3096502673 @default.
• W4377019632 cites W3097841683 @default.
• W4377019632 cites W3119855172 @default.
• W4377019632 cites W3125477777 @default.
• W4377019632 cites W3125973603 @default.
• W4377019632 cites W3145782339 @default.
• W4377019632 cites W3154595990 @default.
• W4377019632 cites W3158568493 @default.
• W4377019632 cites W3176271734 @default.
• W4377019632 cites W3184617284 @default.
• W4377019632 cites W3193926122 @default.
• W4377019632 cites W3196661916 @default.
• W4377019632 cites W3197300698 @default.
• W4377019632 cites W3213325536 @default.
• W4377019632 cites W3213767478 @default.
• W4377019632 cites W3215752673 @default.
• W4377019632 cites W4214547251 @default.
• W4377019632 cites W4214638371 @default.
• W4377019632 cites W4292367830 @default.
• W4377019632 cites W4297534114 @default.
• W4377019632 doi "https://doi.org/10.1080/1206212x.2023.2212945" @default.
• W4377019632 hasPublicationYear "2023" @default.
• W4377019632 type Work @default.
• W4377019632 citedByCount "1" @default.
• W4377019632 countsByYear W43770196322023 @default.
• W4377019632 crossrefType "journal-article" @default.
• W4377019632 hasAuthorship W4377019632A5015354496 @default.
• W4377019632 hasAuthorship W4377019632A5054276800 @default.
• W4377019632 hasAuthorship W4377019632A5087584441 @default.
• W4377019632 hasConcept C108583219 @default.
• W4377019632 hasConcept C120665830 @default.
• W4377019632 hasConcept C121332964 @default.
• W4377019632 hasConcept C124504099 @default.
• W4377019632 hasConcept C138885662 @default.
• W4377019632 hasConcept C142575187 @default.
• W4377019632 hasConcept C153180895 @default.
• W4377019632 hasConcept C154945302 @default.
• W4377019632 hasConcept C2776401178 @default.
• W4377019632 hasConcept C31972630 @default.
• W4377019632 hasConcept C41008148 @default.
• W4377019632 hasConcept C41895202 @default.
• W4377019632 hasConcept C45347329 @default.
• W4377019632 hasConcept C50644808 @default.
• W4377019632 hasConcept C81363708 @default.
• W4377019632 hasConcept C89600930 @default.
• W4377019632 hasConceptScore W4377019632C108583219 @default.
• W4377019632 hasConceptScore W4377019632C120665830 @default.
• W4377019632 hasConceptScore W4377019632C121332964 @default.
• W4377019632 hasConceptScore W4377019632C124504099 @default.
• W4377019632 hasConceptScore W4377019632C138885662 @default.
• W4377019632 hasConceptScore W4377019632C142575187 @default.
• W4377019632 hasConceptScore W4377019632C153180895 @default.
• W4377019632 hasConceptScore W4377019632C154945302 @default.
• W4377019632 hasConceptScore W4377019632C2776401178 @default.
• W4377019632 hasConceptScore W4377019632C31972630 @default.
• W4377019632 hasConceptScore W4377019632C41008148 @default.
• W4377019632 hasConceptScore W4377019632C41895202 @default.
• W4377019632 hasConceptScore W4377019632C45347329 @default.
• W4377019632 hasConceptScore W4377019632C50644808 @default.
• W4377019632 hasConceptScore W4377019632C81363708 @default.
• W4377019632 hasConceptScore W4377019632C89600930 @default.
• W4377019632 hasIssue "5" @default.
• W4377019632 hasLocation W43770196321 @default.
• W4377019632 hasOpenAccess W4377019632 @default.
• W4377019632 hasPrimaryLocation W43770196321 @default.
• W4377019632 hasRelatedWork W2295021132 @default.
• W4377019632 hasRelatedWork W2731899572 @default.
• W4377019632 hasRelatedWork W2960184797 @default.
• W4377019632 hasRelatedWork W3133861977 @default.
• W4377019632 hasRelatedWork W3148519004 @default.
• W4377019632 hasRelatedWork W3212213449 @default.
• W4377019632 hasRelatedWork W4200173597 @default.
• W4377019632 hasRelatedWork W4285827401 @default.
• W4377019632 hasRelatedWork W4312417841 @default.
• W4377019632 hasRelatedWork W4321369474 @default.

• next