UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

Anindya Apriliyanti Pravitasari, Nur Iriawan*, Mawanda Almuhayar, Taufik Azmi, Irhamah, Kartika Fithriasari, Santi Wulan Purnami, Widiana Ferriastuti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)


A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.

Original languageEnglish
Pages (from-to)1310-1318
Number of pages9
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number3
Publication statusPublished - 2020
Externally publishedYes


  • Fully convolution network
  • Image segmentation
  • Transfer learning
  • U-Net
  • VGG16


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