Matches in SemOpenAlex for { <https://semopenalex.org/work/W3210323061> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W3210323061 endingPage "1052" @default.
- W3210323061 startingPage "1029" @default.
- W3210323061 abstract "Abstract This paper plans to develop the optimal brain tumor classification model with diverse intelligent methods. The main phases of the proposed model are ‘(a) image pre-processing, (b) skull stripping, (c) tumor segmentation, (d) feature extraction and (e) classification’. At first, pre-processing of the image is performed by converting the image from red green blue to gray followed by median filtering. Further, skull stripping is done for removing the extra-meningeal tissue from the head image, which is done by Otsu thresholding. As the main contribution, the tumor segmentation is done by the optimized threshold-based tumor segmentation using multi-objective randomly updated beetle swarm and multi-verse optimization (RBS-MVO). The objective constraints considered for the segmentation of the tumor is entropy and variance. Next, the feature extraction techniques like gray level co-occurrence matrix, local binary pattern and gray-level run length matrix is accomplished to extract the set of features. The classification side uses the combination of neural network (NN) and deep learning model called convolutional neural network (CNN) for tumor classification. The extracted features are subjected to NN, and the segmented image is taken as input to CNN. In addition, the weight function of NN and hidden neurons of CNN is optimized by the RBS-MVO." @default.
- W3210323061 created "2021-11-08" @default.
- W3210323061 creator A5006369373 @default.
- W3210323061 creator A5061512880 @default.
- W3210323061 date "2021-10-29" @default.
- W3210323061 modified "2023-09-26" @default.
- W3210323061 title "A Multi-Objective Randomly Updated Beetle Swarm and Multi-Verse Optimization for Brain Tumor Segmentation and Classification" @default.
- W3210323061 cites W1846171835 @default.
- W3210323061 cites W1980658573 @default.
- W3210323061 cites W2031183907 @default.
- W3210323061 cites W2032861264 @default.
- W3210323061 cites W2042445852 @default.
- W3210323061 cites W2072577236 @default.
- W3210323061 cites W2077092075 @default.
- W3210323061 cites W2077913352 @default.
- W3210323061 cites W2094689349 @default.
- W3210323061 cites W2103496373 @default.
- W3210323061 cites W2313516491 @default.
- W3210323061 cites W2417707957 @default.
- W3210323061 cites W2507548850 @default.
- W3210323061 cites W2729859843 @default.
- W3210323061 cites W2739283086 @default.
- W3210323061 cites W2774915117 @default.
- W3210323061 cites W2790857113 @default.
- W3210323061 cites W2799903018 @default.
- W3210323061 cites W2805969589 @default.
- W3210323061 cites W2885730180 @default.
- W3210323061 cites W2891118631 @default.
- W3210323061 cites W2897188827 @default.
- W3210323061 cites W2899901712 @default.
- W3210323061 cites W2918361313 @default.
- W3210323061 cites W2921483513 @default.
- W3210323061 cites W2966634653 @default.
- W3210323061 cites W2980292633 @default.
- W3210323061 cites W2984619469 @default.
- W3210323061 cites W2991522674 @default.
- W3210323061 cites W3002769598 @default.
- W3210323061 cites W3013531267 @default.
- W3210323061 cites W3041046995 @default.
- W3210323061 cites W3087969957 @default.
- W3210323061 cites W3149929842 @default.
- W3210323061 doi "https://doi.org/10.1093/comjnl/bxab171" @default.
- W3210323061 hasPublicationYear "2021" @default.
- W3210323061 type Work @default.
- W3210323061 sameAs 3210323061 @default.
- W3210323061 citedByCount "3" @default.
- W3210323061 countsByYear W32103230612022 @default.
- W3210323061 countsByYear W32103230612023 @default.
- W3210323061 crossrefType "journal-article" @default.
- W3210323061 hasAuthorship W3210323061A5006369373 @default.
- W3210323061 hasAuthorship W3210323061A5061512880 @default.
- W3210323061 hasConcept C115961682 @default.
- W3210323061 hasConcept C124504099 @default.
- W3210323061 hasConcept C153180895 @default.
- W3210323061 hasConcept C154945302 @default.
- W3210323061 hasConcept C191178318 @default.
- W3210323061 hasConcept C41008148 @default.
- W3210323061 hasConcept C50644808 @default.
- W3210323061 hasConcept C52622490 @default.
- W3210323061 hasConcept C81363708 @default.
- W3210323061 hasConcept C89600930 @default.
- W3210323061 hasConceptScore W3210323061C115961682 @default.
- W3210323061 hasConceptScore W3210323061C124504099 @default.
- W3210323061 hasConceptScore W3210323061C153180895 @default.
- W3210323061 hasConceptScore W3210323061C154945302 @default.
- W3210323061 hasConceptScore W3210323061C191178318 @default.
- W3210323061 hasConceptScore W3210323061C41008148 @default.
- W3210323061 hasConceptScore W3210323061C50644808 @default.
- W3210323061 hasConceptScore W3210323061C52622490 @default.
- W3210323061 hasConceptScore W3210323061C81363708 @default.
- W3210323061 hasConceptScore W3210323061C89600930 @default.
- W3210323061 hasIssue "4" @default.
- W3210323061 hasLocation W32103230611 @default.
- W3210323061 hasOpenAccess W3210323061 @default.
- W3210323061 hasPrimaryLocation W32103230611 @default.
- W3210323061 hasRelatedWork W2018206842 @default.
- W3210323061 hasRelatedWork W2045391057 @default.
- W3210323061 hasRelatedWork W2059299633 @default.
- W3210323061 hasRelatedWork W2181351615 @default.
- W3210323061 hasRelatedWork W2347731544 @default.
- W3210323061 hasRelatedWork W2350588503 @default.
- W3210323061 hasRelatedWork W2406522397 @default.
- W3210323061 hasRelatedWork W2551390060 @default.
- W3210323061 hasRelatedWork W2732542196 @default.
- W3210323061 hasRelatedWork W3116883888 @default.
- W3210323061 hasVolume "65" @default.
- W3210323061 isParatext "false" @default.
- W3210323061 isRetracted "false" @default.
- W3210323061 magId "3210323061" @default.
- W3210323061 workType "article" @default.