Performance comparison of deep learning models for MRI-based brain tumor detection

Abstract

Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.