Paper Contents
Abstract
With brain tumors being one of the deadliest forms of cancer, affecting around 700,000 people and causing 16,830 deaths since 2019, early and precise diagnosis is crucial. This research focuses on enhancing brain segmentation and classification in MRI scans using the YOLOv8 deep learning model. Traditional YOLO-based models excel in real-time object detection but lack precision in segmenting tumor boundaries. The proposed system integrates segmentation capabilities within YOLOv8, leveraging advanced convolutional techniques and spatial pyramid pooling for improved accuracy. The model is trained on annotated MRI datasets, producing detailed tumor masks to aid clinical diagnosis and treatment planning. Key advantages include real-time processing, enhanced tumor visualization, and robustness against image noise.
Copyright
Copyright © 2025 SURYA NARAYANAN. This is an open access article distributed under the Creative Commons Attribution License.
