Glioma Brain Tumor Classification using Transfer Learning

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dc.contributor.authorPilaoon, Pongsak
dc.contributor.authorNarkthewan, Athasart
dc.contributor.authorWadlom, Noppanat
dc.contributor.authorVarakulsiripunth, Ruttikorn
dc.contributor.authorHamamoto, Kazuhiko
dc.contributor.authorManeerat, Noppadol
dc.date.accessioned2026-08-06T10:44:20Z
dc.date.available2026-08-06T10:44:20Z
dc.date.issued2024-01-01
dc.description.abstractIn this research the glioma brain tumor binary classification using transfer learning was introduced. The MRI image from 2 datasets comprised with REMBRANDT and BraTS2021 with increasing number of MRI images was proposed to prevent overfitting problem. MRI images were converted to JPEG format and heavily imbalanced with normal brain image is minority class. Morphological operation was used to remove skeletons and artifacts from brain region. We have introduced Contrast Limited Adaptive Histogram Equalization to preprocess and enhance contrast before classify using various CNNs. To handle imbalanced dataset problem, we proposed image augmentation to increase the number of images and obtain balanced dataset. The various CNNs transfer learning was implemented to classify glioma brain tumor. Finally, the best classifier is InceptionV3 with balanced dataset that obtained accuracy 99.19%, sensitivity 98.83%, and specificity 100% respectively, better than our past research work.
dc.identifier.citation2024 10th International Conference on Engineering Applied Sciences and Technology Iceast 2024, 17-22, 2024
dc.identifier.doi10.1109/ICEAST61342.2024.10553834
dc.identifier.other2-s2.0-85197277136
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15173
dc.source2024 10th International Conference on Engineering Applied Sciences and Technology Iceast 2024
dc.subjectCLAHE
dc.subjectConvolution Neural Network
dc.subjectGlioma Brain Tumor
dc.subjectmorphological operation
dc.subjectTransfer Learning
dc.titleGlioma Brain Tumor Classification using Transfer Learning
dc.typeConference Paper

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