Abstract

The use of brain tumor detection using the classification approach becomes increasingly relevant as technology advances. The accuracy with which brain tumors are identified can influence the treatment offered to patients. The manual segmentation have complexity, and subjectivity from the expertise, and needed a lot of time to choose the treatment. Thus, Brain tumor segmentation research is also gaining challenges, although the methods used to do so often need a lot of memory and calculations. To prevent computational stress and amass image data for deeper learning, we adopt the CNN U-Net 2D approach with using skip connection, dropout, and convolutional transpose to decreased the consuming time, and automatically segmentation the location of the brain tumor. BRATS2018, and BRATS2020 is used in this research to gain the optimum value our model. As a consequence, the precision, recall, and fl-score for complete tumor, outcomes in this investigation were 0.92, 0.91, 0.92, and 0.85 in BRATS2020, and 0.90, 0.90, 0.90 in BRATS2018.

Original languageEnglish
Title of host publication2022 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationAdvanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
Number of pages6
ISBN (Electronic)9781665460811
DOIs
Publication statusPublished - 2022
Event23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022 - Virtual, Surabaya, Indonesia
Duration: 20 Jul 202221 Jul 2022

Publication series

Name2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding

Conference

Conference23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period20/07/2221/07/22

Keywords

  • Brain Tumor
  • Segmentation
  • U-Net

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