U-Net for Medical Imaging: A Novel Approach for Brain Tumor Segmentation
DOI:
https://doi.org/10.58260/j.iet.2202.0104Keywords:
Automatic, MRI images, U-Net, Brain Tumor, Machine Learning, SegmentationAbstract
In medical imaging, brain tumor segmentation is critical. The segmentation of a brain tumor might be done manually or automatically. Finding anomalies in magnetic resonance imaging (MRI) images manually is time-consuming and complex. Automatic segmentation, on the other hand, is incredibly accurate and time-saving. Any technique that can detect a brain tumor early would improve the diagnosis method. As a result, the number of cases of death will decrease. MRI (Magnetic Resonance Imaging) scans have proven to be quite helpful in the detection and segmentation of brain tumors in recent years. MRI images can aid in the detection of a brain tumor. MRI scans can detect abnormal tissue growth and blood blockages in the neurological system. The U-Net model is being used to segment the brain tumor region. The U-Net model is simply a more advanced version of CNN's algorithm. The U-Net model was created to segment biological pictures. We create a 3D U-Net design to segment the brain tumor infection zone in this paper. We combine clinical data with novel radiometric parameters based on the geometry, position, and shape of the segmented tumor to estimate each patient's survival length. The loss graph and accuracy graph are given together with the scores. Finally, we run the tests on various original photographs using the masks that correspond to them.
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