Abstract:Abstract Brain tumors are lumps or clusters of abnormal cells that can develop. They pose a risk to the patient due to their capacity to infiltrate othe tissues.Due to the difficulty of tumor biopsy for patients with brain tumors,noninvasive imaging techniques, such as Magnetic Resonance Imaging (MRI),have been extensively employed in the diagnosis of brain tumors.The rapid advancement of Deep Learning techniques for computer vision applications has been enabled by a large quantity of training data and enhancements in model architecture that provide superior approximations in a supervised context. Introduction: Medical imaging, of which brain tumor detection is an essential part, plays an essential role in the detection and treatment of brain diseases. Traditional methods of identifying tumors include manual analysis of medical images, which can be challenging and susceptible to human error [1]. The below is the image shows the brain tumors:In this left Image depicts a large tumor on the surface of the brain. The tumor is located in the outer layers, potentially pressing on the surrounding brain tissue. This could represent a mass effect caused by the tumor, which can lead to neurological symptoms due to the increased pressure on the brain.Similarly, middle image shows a tumor that is more embedded within the brain tissue. This suggests a deep-seated tumor that might be harder to access surgically and could disrupt nearby brain functions depending on its location.Lasly, right image shows a smaller tumor that may involve some nerves or critical brain areas. The illustration could be highlighting a case where the tumor is located near essential brain structures, like nerves or blood vessels, making the condition more complex and potentially causing specific symptoms depending on the area affected. Problem Statement: The precise identification and classification of brain tumors are essential for patient diagnosis and treatment planning. Manually interpreting medical images, such as MRI scans, can be difficult and subjective, increasing the risk of errors and delays in patient care. Therefore, an automatic and reliable method for identifying and categorizing brain tumors from medical imaging is required using computer vision techniques such as noise reduction,tumor segmentation and edge detection[2] References [1] M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, “Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging,” Sensors, no. 5, p. 1960, Mar. 2022, doi: 10.3390/s22051960. [2] Manfield JH, Yu KKH. Real-time ultrasound-guided external ventricular drain placement: technical note. Neurosurg Focus. 2017 Nov;43(5):E5. doi: 10.3171/2017.7.FOCUS17148. PMID: 29088955. [3] Data resources:https://figshare.com/articles/dataset/braintumordataset/15124