A Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation

A brain tumor is an extremely hazardous illness that can affect people of any age. Less than 50% of individuals with brain cancer have a chance of surviving. As a result, precise segmentation of brain tumors is crucial for the diagnosis, planning of the course of treatment, and tracking of the tumor growth. Deep Learning (DL) models can increase the precision and speed of brain tumor diagnosis by precisely segmenting and identifying tumor locations in medical pictures. In this study, we compare four DL models for segmenting brain tumors, the 3D U-Net, the Attention Res U-Net, the U-Net++, and the U-Net Transformer (UNETR). We used 485 MRI (Magnetic Resonance Imaging) scans from the BraTS 2018 dataset, which include annotated ground truth tumor segmentations. We carried out preprocessing operations such as label merging, cropping, and z-score normalization. We evaluated the performance of two models using the dice coefficient metric. Our findings demonstrated that the Attention Res U-Net has a higher segmentation accuracy than the other three U-Net models, with a testing dice coefficient of 0.79 against 0.78, 0.77, 0.72 for the 3D U-net, UNETR, and U-net++ respectively. The results point to the Attention Res U-Net as a potentially useful method for brain tumor segmentation tasks.