New studies have shown that using edge extraction with Deep Learning processes may greatly enhance performance while looking for brain tumours. Though deep learning and brain tumour detection have been the subject of some study, poor performance remains a major obstacle. The current deep learning approach to identifying brain tumours was sped up by adding edge detection. When a pre-trained network is used, tumour detection may be completed in half the time. The MRI scans have undergone a deep learning training process. The brain looks like a wall clock, at least according to a dataset used to train deep neural networks. If tumors take the shape of a dish, they may be easily detected. This research presents a discussion of the use of deep learning and reinforcement learning to identify brain cancers. The current states of the field, as well as its features and operational methodology, have been discussed. Given the present state of knowledge, finding brain tumours may be difficult. This evaluation will act as a jumping-off point for further research. It has been shown that edge detection is required to improve system performance. This has led to the introduction of edge detection and its extraction, along with its operational method and many types. The next steps for the study have been defined

Brain tumor Detection, image processing, edge detection, edge extraction