Empowered Brain Tumor Detection Using Deep Learning Methodology

In medical diagnosis, early and accurate detection of brain tumors is crucial for effective treatment planning and improved patient outcomes. Traditional methods for brain tumor detection in magnetic resonance imaging (MRI) scans and computed tomography (CT) scans can be time-consuming, subjective, and prone to human error. In this paper we overcome the problem in the domain of computer vision with deep learning approach. Deep learning models have emerged as a promising alternative, offering high accuracy and the potential for automation. This work investigates the application of the YOLOv8 deep learning model for real-time brain tumor detection in MRI scans. YOLOv8 offers a compelling balance between speed and accuracy, making it suitable for deployment in clinical settings. We evaluate the performance of YOLOv8 on a benchmark brain tumor dataset and compare it with other commonly used models. The results demonstrate that our YOLOv8 model achieves competitive accuracy (mentioning the specific metrics like accuracy, precision, recall) while maintaining real-time inference speed, paving the way for its potential use in real-time brain tumor detection during clinical examinations.