FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288949265> ?p ?o ?g. }

• W4288949265 endingPage "104018" @default.
• W4288949265 startingPage "104018" @default.
• W4288949265 abstract "Glioma is the most common brain tumor in humans. Accurate stage estimation of the tumor is essential for treatment planning. The biopsy is the gold standard method for this purpose. However, it is an invasive procedure, which can prove fatal for patients, if a tumor is present deep inside the brain. Therefore, a magnetic resonance imaging (MRI) based non-invasive method is proposed in this paper for low-grade glioma (LGG) versus high-grade glioma (HGG) classification. To maximize the above classification performance, five pre-trained convolutional neural networks (CNNs) such as AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 are assembled using a majority voting mechanism. Segmentation methods require human intervention and additional computational efforts. It makes computer-aided diagnosis tools semi-automated. To analyze the performance effect of segmentation methods, three segmentation methods such as region of interest MRI segmentation (RSM) and skull-stripped MRI segmentation (SSM), and whole-brain MRI (WBM) (non-segmentation) data were compared using above mentioned algorithm. The highest classification accuracy of 99.06 ± 0.55 % was observed on the RSM data and the lowest accuracy of 98.43 ± 0.89 % was observed on the WSM data. However, only a 0.63 % improvement was found in the accuracy of the RSM data against the WBM data. This shows that deep learning models have an incredible ability to extract appropriate features from images. Furthermore, the proposed algorithm showed 2.85 %, 1.39 %, 1.26 %, 2.66 %, and 2.33 % improvement in the average accuracy of the above three datasets over the AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models, respectively." @default.
• W4288949265 created "2022-07-31" @default.
• W4288949265 creator A5001113051 @default.
• W4288949265 creator A5035444159 @default.
• W4288949265 creator A5086308451 @default.
• W4288949265 date "2022-09-01" @default.
• W4288949265 modified "2023-10-16" @default.
• W4288949265 title "Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm" @default.
• W4288949265 cites W112971866 @default.
• W4288949265 cites W1996908572 @default.
• W4288949265 cites W2021787435 @default.
• W4288949265 cites W2031604000 @default.
• W4288949265 cites W2040827479 @default.
• W4288949265 cites W2055957434 @default.
• W4288949265 cites W2083927153 @default.
• W4288949265 cites W2088897029 @default.
• W4288949265 cites W2097117768 @default.
• W4288949265 cites W2103243046 @default.
• W4288949265 cites W2116531017 @default.
• W4288949265 cites W2119479641 @default.
• W4288949265 cites W2165698076 @default.
• W4288949265 cites W2274425329 @default.
• W4288949265 cites W2293496672 @default.
• W4288949265 cites W2366536035 @default.
• W4288949265 cites W2588978745 @default.
• W4288949265 cites W2620727918 @default.
• W4288949265 cites W2779494124 @default.
• W4288949265 cites W2884741638 @default.
• W4288949265 cites W2888749348 @default.
• W4288949265 cites W2897188827 @default.
• W4288949265 cites W2900530921 @default.
• W4288949265 cites W2909533222 @default.
• W4288949265 cites W2919115771 @default.
• W4288949265 cites W2919392656 @default.
• W4288949265 cites W2921601294 @default.
• W4288949265 cites W2945839551 @default.
• W4288949265 cites W2955805844 @default.
• W4288949265 cites W2983509244 @default.
• W4288949265 cites W2995378448 @default.
• W4288949265 cites W3001670762 @default.
• W4288949265 cites W3011430986 @default.
• W4288949265 cites W3031839920 @default.
• W4288949265 cites W3036138508 @default.
• W4288949265 cites W3036691346 @default.
• W4288949265 cites W3045522357 @default.
• W4288949265 cites W3047434002 @default.
• W4288949265 cites W3084741591 @default.
• W4288949265 cites W3095417235 @default.
• W4288949265 cites W3175625159 @default.
• W4288949265 cites W4281751621 @default.
• W4288949265 doi "https://doi.org/10.1016/j.bspc.2022.104018" @default.
• W4288949265 hasPublicationYear "2022" @default.
• W4288949265 type Work @default.
• W4288949265 citedByCount "7" @default.
• W4288949265 countsByYear W42889492652022 @default.
• W4288949265 countsByYear W42889492652023 @default.
• W4288949265 crossrefType "journal-article" @default.
• W4288949265 hasAuthorship W4288949265A5001113051 @default.
• W4288949265 hasAuthorship W4288949265A5035444159 @default.
• W4288949265 hasAuthorship W4288949265A5086308451 @default.
• W4288949265 hasConcept C108583219 @default.
• W4288949265 hasConcept C126838900 @default.
• W4288949265 hasConcept C142724271 @default.
• W4288949265 hasConcept C143409427 @default.
• W4288949265 hasConcept C153180895 @default.
• W4288949265 hasConcept C154945302 @default.
• W4288949265 hasConcept C2778227246 @default.
• W4288949265 hasConcept C2779130545 @default.
• W4288949265 hasConcept C41008148 @default.
• W4288949265 hasConcept C502942594 @default.
• W4288949265 hasConcept C71924100 @default.
• W4288949265 hasConcept C81363708 @default.
• W4288949265 hasConcept C89600930 @default.
• W4288949265 hasConceptScore W4288949265C108583219 @default.
• W4288949265 hasConceptScore W4288949265C126838900 @default.
• W4288949265 hasConceptScore W4288949265C142724271 @default.
• W4288949265 hasConceptScore W4288949265C143409427 @default.
• W4288949265 hasConceptScore W4288949265C153180895 @default.
• W4288949265 hasConceptScore W4288949265C154945302 @default.
• W4288949265 hasConceptScore W4288949265C2778227246 @default.
• W4288949265 hasConceptScore W4288949265C2779130545 @default.
• W4288949265 hasConceptScore W4288949265C41008148 @default.
• W4288949265 hasConceptScore W4288949265C502942594 @default.
• W4288949265 hasConceptScore W4288949265C71924100 @default.
• W4288949265 hasConceptScore W4288949265C81363708 @default.
• W4288949265 hasConceptScore W4288949265C89600930 @default.
• W4288949265 hasLocation W42889492651 @default.
• W4288949265 hasOpenAccess W4288949265 @default.
• W4288949265 hasPrimaryLocation W42889492651 @default.
• W4288949265 hasRelatedWork W2731899572 @default.
• W4288949265 hasRelatedWork W2790662084 @default.
• W4288949265 hasRelatedWork W2999805992 @default.
• W4288949265 hasRelatedWork W3116150086 @default.
• W4288949265 hasRelatedWork W3133861977 @default.
• W4288949265 hasRelatedWork W4200173597 @default.
• W4288949265 hasRelatedWork W4225304418 @default.
• W4288949265 hasRelatedWork W4287009405 @default.
• W4288949265 hasRelatedWork W4312417841 @default.

• next