Deep learning-based brain tumor segmentation: A comparison of U-Net and segNet algorithms

Brain tumors are among the diseases that pose a serious health concern worldwide and can lead to fatal outcomes if left untreated. The segmentation of brain tumors is a critical step for the accurate diagnosis of the disease and effective management of the treatment process. This study was conducted to examine the success rates of deep learning-based U-Net and SegNet algorithms in brain tumor segmentation. MRI brain images and black and white masks belonging to these images were used in the study. Image processing techniques, including histogram equalization, edge detection, noise reduction, contrast enhancement, and Gaussian blurring, were applied. These image processing steps improved the quality of the MRI images, contributing to more accurate segmentation results. As a result of the segmentation operations performed with U-Net and SegNet algorithms, the U-Net algorithm achieved an accuracy rate of 96%, while the SegNet algorithm’s accuracy rate was measured at 94%. The study determined that the U-Net algorithm provided a higher success rate and was more effective in brain tumor segmentation. In particular, the contribution of image processing steps to segmentation success was observed.