Matches in SemOpenAlex for { <https://semopenalex.org/work/W4301600543> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4301600543 endingPage "660" @default.
- W4301600543 startingPage "660" @default.
- W4301600543 abstract "Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%." @default.
- W4301600543 created "2022-10-05" @default.
- W4301600543 creator A5017464259 @default.
- W4301600543 creator A5019126566 @default.
- W4301600543 creator A5047367276 @default.
- W4301600543 creator A5052630290 @default.
- W4301600543 creator A5069110017 @default.
- W4301600543 date "2022-09-30" @default.
- W4301600543 modified "2023-09-30" @default.
- W4301600543 title "Convolutional Neural Network featuring VGG-16 Model for Glioma Classification" @default.
- W4301600543 doi "https://doi.org/10.30630/joiv.6.3.1230" @default.
- W4301600543 hasPublicationYear "2022" @default.
- W4301600543 type Work @default.
- W4301600543 citedByCount "0" @default.
- W4301600543 crossrefType "journal-article" @default.
- W4301600543 hasAuthorship W4301600543A5017464259 @default.
- W4301600543 hasAuthorship W4301600543A5019126566 @default.
- W4301600543 hasAuthorship W4301600543A5047367276 @default.
- W4301600543 hasAuthorship W4301600543A5052630290 @default.
- W4301600543 hasAuthorship W4301600543A5069110017 @default.
- W4301600543 hasBestOaLocation W43016005431 @default.
- W4301600543 hasConcept C108583219 @default.
- W4301600543 hasConcept C115961682 @default.
- W4301600543 hasConcept C119857082 @default.
- W4301600543 hasConcept C121608353 @default.
- W4301600543 hasConcept C126322002 @default.
- W4301600543 hasConcept C126838900 @default.
- W4301600543 hasConcept C142724271 @default.
- W4301600543 hasConcept C143409427 @default.
- W4301600543 hasConcept C153180895 @default.
- W4301600543 hasConcept C154945302 @default.
- W4301600543 hasConcept C188441871 @default.
- W4301600543 hasConcept C22019652 @default.
- W4301600543 hasConcept C2776145597 @default.
- W4301600543 hasConcept C2778227246 @default.
- W4301600543 hasConcept C2779130545 @default.
- W4301600543 hasConcept C2994463257 @default.
- W4301600543 hasConcept C41008148 @default.
- W4301600543 hasConcept C502942594 @default.
- W4301600543 hasConcept C50644808 @default.
- W4301600543 hasConcept C71924100 @default.
- W4301600543 hasConcept C75294576 @default.
- W4301600543 hasConcept C81363708 @default.
- W4301600543 hasConceptScore W4301600543C108583219 @default.
- W4301600543 hasConceptScore W4301600543C115961682 @default.
- W4301600543 hasConceptScore W4301600543C119857082 @default.
- W4301600543 hasConceptScore W4301600543C121608353 @default.
- W4301600543 hasConceptScore W4301600543C126322002 @default.
- W4301600543 hasConceptScore W4301600543C126838900 @default.
- W4301600543 hasConceptScore W4301600543C142724271 @default.
- W4301600543 hasConceptScore W4301600543C143409427 @default.
- W4301600543 hasConceptScore W4301600543C153180895 @default.
- W4301600543 hasConceptScore W4301600543C154945302 @default.
- W4301600543 hasConceptScore W4301600543C188441871 @default.
- W4301600543 hasConceptScore W4301600543C22019652 @default.
- W4301600543 hasConceptScore W4301600543C2776145597 @default.
- W4301600543 hasConceptScore W4301600543C2778227246 @default.
- W4301600543 hasConceptScore W4301600543C2779130545 @default.
- W4301600543 hasConceptScore W4301600543C2994463257 @default.
- W4301600543 hasConceptScore W4301600543C41008148 @default.
- W4301600543 hasConceptScore W4301600543C502942594 @default.
- W4301600543 hasConceptScore W4301600543C50644808 @default.
- W4301600543 hasConceptScore W4301600543C71924100 @default.
- W4301600543 hasConceptScore W4301600543C75294576 @default.
- W4301600543 hasConceptScore W4301600543C81363708 @default.
- W4301600543 hasIssue "3" @default.
- W4301600543 hasLocation W43016005431 @default.
- W4301600543 hasOpenAccess W4301600543 @default.
- W4301600543 hasPrimaryLocation W43016005431 @default.
- W4301600543 hasRelatedWork W2504770560 @default.
- W4301600543 hasRelatedWork W2766604260 @default.
- W4301600543 hasRelatedWork W2767651786 @default.
- W4301600543 hasRelatedWork W2945461009 @default.
- W4301600543 hasRelatedWork W2981481749 @default.
- W4301600543 hasRelatedWork W3118394139 @default.
- W4301600543 hasRelatedWork W4220996320 @default.
- W4301600543 hasRelatedWork W4283701629 @default.
- W4301600543 hasRelatedWork W4285802257 @default.
- W4301600543 hasRelatedWork W4301600543 @default.
- W4301600543 hasVolume "6" @default.
- W4301600543 isParatext "false" @default.
- W4301600543 isRetracted "false" @default.
- W4301600543 workType "article" @default.