Impact of Data-Augmentation on Brain Tumor Detection Using Different YOLO Versions Models

Brain tumors are widely recognized as one of the world's worst and most disabling diseases. Every year, thousands of people die as a result of brain tumors caused by the rapid growth of tumor cells. As a result, saving the lives of tens of thousands of people worldwide needs speedy investigation and automatic identification of brain tumors. In this paper, we propose a new methodology for detecting brain tumors. The designed framework assesses the application of cutting -edge YOLO models such as YOLOv3, YOLO v5n, YOLO v5s, YOLO v5m, YOLOv5l, YOLOv5x, and YOLOv7 with varying weights and data augmentation on a dataset of 7382 samples from three distinct MRI orientations, namely, axial, coronal, and sagittal. Several data augmentation techniques were also employed to minimize detector sensitivity while increasing detection accuracy. In addition, the Adam and Stochastic Gradient Descent (SGD) optimizers were compared. We aim to find the ideal network weight and MRI orientation for detecting brain cancers. The results show that with an IoU of 0.5, axial orientation had the highest detection accuracy with an average mAP of 97.33%. Furthermore, SGD surpasses Adam optimizer by more than 20% mAP. Also, it was found that YOLO 5n, YOLOv5s, YOLOv5x, and YOLOv3 surpass others by more than 95% mAP. Besides that, it was observed that the YOLOv5 and YOLOv3 models are more sensitive to data augmentation than the YOLOv7 model.