Brain Tumor Detection Instance Segmentation Using YOLOv8 in Medical Imaging

Brain tumors present a significant challenge in medical diagnostics due to their complex nature and the critical need for precise detection. This paper introduces a novel approach to brain tumor detection using instance segmentation with the advanced YOLOv8 model in medical imaging. We first outline the limitations of current imaging techniques in accurately identifying brain tumors. Then, we detail our methodology, which includes a comprehensive data preprocessing strategy tailored for medical imaging, followed by an in-depth explanation of the customized YOLOv8 architecture used for this study. Our training and validation process is thoroughly explained, emphasizing the adaptations made for handling medical datasets. The results section demonstrates the model's efficacy through various metrics, including precision with 0.99%, recall 0.90%, and mAP Analysis 0.95%, showcasing significant improvements over existing methods. The paper concludes by emphasizing the potential impact of applying YOLOv8 in medical imaging for brain tumor detection, suggesting a substantial step forward in oncological diagnostics and patient care.