文章基本信息

标题：UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation
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作者：Anindya Apriliyanti Pravitasari ; Nur Iriawan ; Mawanda Almuhayar 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2020
卷号：18
期号：3
页码：1310-1318
DOI：10.12928/telkomnika.v18i3.14753
出版社：Universitas Ahmad Dahlan
摘要：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%.
关键词：fully convolution network; image segmentation; transfer learning; U-net; VGG16;