Evaluation of Deep Learning Based YOLOv3 Brain Tumor Classification Performance Using Magnetic Resonance Imaging

Abstract

Magnetic resonance imaging (MRI) provides high-resolution images of soft tissues and is an imaging technique with a high diagnostic value. It can play a role in computer-aided diagnosis through deep learning technologies using digital data. This study aims to investigate the performance of brain tumor classification using YOLOv3 based on deep learning. Deep learning was performed using 253 open MRI images in which the learning evaluation indices were average loss, region 82, and region 94. The detection performance was evaluated using images that were not used for training to verify the brain tumor classification model. The average loss was 0.1107 for 2248 epochs. After 24,079 learning iterations, average IoU, class, .5R, and .75R were 0.89, 0.81, 1.00, and 1.00 for region 82, and 1.00, 1.00, 1.00, and 1.00 for region 94, respectively. Owing to the verification of the brain tumor classification model, it was possible to classify normal brain and brain tumors with an accuracy of 95.00% and 75.36%, respectively. It is believed that the results of this study will be used as basic data for deep learning research and clinical trials using MRI images.