Analysis of Detection of Glioma by Segmentation of Brain Tumor MRI Images Using Deep Learning

Segmenting a brain tumor is a crucial step in medical image processing. Early detection of brain tumors is crucial for increasing the effectiveness of treatment options and patient survival. To manually diagnose cancer out of brain tumor images is not only challenging but also time-consuming as there are large amounts of MRI images that have to be scanned daily in clinical practice. Therefore, automatic brain tumor picture segmentation would be a great help. An overview of MRI-based brain tumor segmentation techniques is presented in this chapter. Deep learning techniques due to their edge on the conventional techniques have become popular in the field of automatic segmentation due to their superior findings. The massive volumes of MRI-based image data can also be processed effectively and evaluated objectively using deep learning techniques. Numerous review articles that concentrate on conventional techniques for MRI-based brain tumor picture segmentation are already available. Unlike other papers, this one concentrates on the current deep learning approaches trend in this area. First, a brief overview of brain tumors and techniques for segmenting them is provided. The most current developments in deep learning algorithms are then reviewed, along with state-of-the-art algorithms. Finally, an evaluation of the existing situation is provided, where these brain tumor segmentation techniques based on MRI images could be standardized and implemented into regular clinical practice.