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All Journal Perfecting a Video Game with Game Metrics
Almuhayar, Mawanda
Institut Teknologi Sepuluh Nopember
Computer Science & IT

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UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation Anindya Apriliyanti Pravitasari; Nur Iriawan; Mawanda Almuhayar; Taufik Azmi; Irhamah Irhamah; Kartika Fithriasari; Santi Wulan Purnami; Widiana Ferriastuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14753

Abstract

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%.
Co-Authors Anindya Apriliyanti Pravitasari Fithriasari, Kartika Irhamah - Nur Iriawan Santi Wulan Purnami Taufik Azmi Widiana Ferriastuti