The development of a brain tumor is regarded as a severe medical condition that can lead to cancer. One of the most common ways to detect this type of tumor is through the use of nuclear magnetic resonance imaging. This type of imaging scans the brain to visualize the abnormal cells. Machine learning and deep learning are being used in the detection of brain tumors. These methods can help the radiologist make quick decisions and deliver effective treatments. Classification of brain tumors using MRI scans is very important for both the diagnosis and treatment of brain cancer. This process can help speed up the planning and treatment of the patient. It can also improve the survival rate of patients. Developing automatic models of brain tumors is also very beneficial for reducing the human factor. In several studies, these methods were shown to improve the accuracy of brain tumor predictions. In recent years, the development of machine learning and deep learning techniques has been widely acknowledged for their potential to improve the performance of various computer vision tasks, such as image classification and semantic segmentation. Due to the technological advancements that have occurred in the field, a comprehensive survey has been conducted to study the various applications of machine learning and deep learning in brain tumor segmentation.

Brain tumor, Machine Learning, Deep Learning, image segmentation