Abstract
Segmenting brain tumors is a major challenge in the production of scientific pictures. To order to maximize care outcomes and increasing the hospital success rate, early detection of brain tumors plays an important part. A challenging and time-consuming job is the manual segmentation of brain tumors from large quantities of MRI images produced in clinical routine. Automatic brain tumor segmentation is possible. This article aims to analyze strategies for the segmentation of brain tumors dependent on MRI. Automatic segmentation using deep learning approaches has recently been proven common because these approaches accomplish the latest findings much better than other methods would solve this issue. Deep learning approaches may also provide for effective analysis and unbiased interpretation of vast volumes of picture evidence dependent on MRI. There are many papers on MRI based brain tumor segmentation which focus on traditional methods. Different from others, we concentrate on the recent trend in the field of deep learning. Next, the brain tumors and techniques for segmenting the brain tumor are added. Then, the new architectures are explored with a emphasis on the current development in deep learning methods. Finally, an evaluation is introduced and further improvements are discussed to standardize brain tumor segmentation procedures dependent on MRI in the day-to-day clinical practice.
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