Performance Comparison of Brain Tumor Segmentation Based on U-Net and ResU-Net Architectural Deep Convolutional Neural Network

Abstract

Brain tumors affect 7 of the 100,000 population and are the 10th leading cause of death in Indonesia. The process of diagnosing brain tumors is done using MRI images which are manually analyzed by radiologists. However, the small number of radiologists with uneven distribution means that the determination of the location and size of the tumor is severely hampered. The solution to this problem is to create technology that can accurately determine the location and size of tumors, one of which is the Deep Convolutional Neural Network. This paper has simulated the location and size of brain tumor segmentation based on MRI images using Deep CNN U-Net architecture and modified it with ResNet (ResU-Net) to make it easier for radiologists to examine the brain accurately. The author has conducted trials with U-Net and ResU-Net architectures to receive the location and size of brain tumors with accurate results from training, validation, and segmentation as measured by the Tanimoto Index, Dice Coefficient, and Tversky Index as evaluation metrics. The author also analyzes the performance of each architecture based on the number of layers, parameters, and training time. Based on these simulation scenarios, the maximum accuracy of the segmentation for U-Net is 88.35% and ResU-Net is 90.04%.