Brain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images

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dc.contributor.authorUtpal Chandra Das
dc.contributor.authorWatit Benjapolakul
dc.contributor.authorManoj Gupta
dc.contributor.authorTimporn Vitoonpong
dc.contributor.authorPannee Suanpang
dc.contributor.authorChanyanan Somthawinpongsai
dc.contributor.authorSujin Butdisuwan
dc.contributor.authorAziz Nanthaamornphong
dc.contributor.correspondenceU.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; email: dasutpal999@gmail.com
dc.date.accessioned2025-03-10T07:34:20Z
dc.date.available2025-03-10T07:34:20Z
dc.date.issued2024
dc.description.abstractThis research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.
dc.identifier.citationIEMECON 2024 - 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks
dc.identifier.doi10.1109/IEMECON62401.2024.10846661
dc.identifier.isbn979-835038731-5
dc.identifier.scopus2-s2.0-85218093732
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4446
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.holderScopus
dc.subjectBrain Cancer
dc.subjectDeep Learning
dc.subjectImage processing
dc.subjectMRI Images
dc.titleBrain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85218093732&doi=10.1109%2fIEMECON62401.2024.10846661&partnerID=40&md5=c5fd2d53aa16e3ca3b14bc911d12b569
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