Hyper Parameters Optimization for Effective Brain Tumor Segmentation with YOLO Deep Learning

Abstract

In the field of neuroimaging, differential diagnosis of brain tumour primarily relies on the visual appearance of the tumor in the MRI. For better analysis of brain tumours segmentation of tumour ROI is indispensable. The objective of this work is to develop a deep network for brain tumour segmentation using YOLO with T1C+ High grade and FLAIR low grade tumour images from the BRATS dataset. As the hyper parameters play a crucial role in the efficacy of the CNN, an exhaustive search is made to arrive at the optimal hyper parameters. The number of anchor boxes, mini batch size and the learning algorithm are carefully evaluated to arrive at the optimal values. The visual analysis of segmentation proves the effectiveness with 4 anchor boxes, mini batch size 16 and Adam optimizer. The mean average precision value of 0.9688 and recall value of 0.9841 is obtained for HGG segmentation. The mean average precision value as 0.8435 and recall value of 0.8552 is obtained for LGG segmentation. The accuracy of the tumor detector computed with Mean IoU score is 100% for HGG and 98% for LGG tumor images. The tumor image segmented using YOLO is further subjected to histogram and region growing techniques to segment the ring and lesion core of the brain tumor which may be used for further analysis of benign and malignant tumor. As YOLO CNN could fix the bounding box with highest accuracy, the traditional skull stripping pre-processing techniques can be skipped for tumor segmentation.