Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation

Abstract

A brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance.