Segmentation of Human Brain Gliomas Tumour Images using U-Net Architecture with Transfer Learning

The complexity of segmenting a brain tumour is critical in medical image processing. Treatment options and patient survival rates can only be improved if brain tumours can be prevented and treated. Segmentation of the brain is the most complex and time-consuming task to diagnose cancer utilizing a manual approach for numerous magnetic resonance images (MRI). The aim of MRI brain tumour image segmentation that to build an automated magnetic resonance imaging tumour segmentation system with separate the area of tumour and provided a clear boundary of the tumour region. U-Nets with different transfer learning models as backbones are presented in this paper, there are ResNet50, DenseNet169 and EfficientNet-B7. Brain lesion segmentation is performed using the multimodal brain tumor segmentation challenge 2020 dataset (BraTS2020). Based on MRI scans of the brain, the tumor segmentation technique is assessed using F1-score, Dice loss, and intersection over union score (IoU). The U-Net encoder used with EfficientNet-B7 outperforms all other architectures in terms of performance metrics across the board. Overall, the results of this experiment are rather excellent. The Dice-loss score was 0.009435, and the score of IoU was 0.7435, F1-score was 0.9848, accuracy was 0.9924, precision was 0.9829, recall was 0.9868, and specificity was 0.9943. The U-Net with EfficientNet-B7 architecture was shown to be crucial in the treatment of brain tumors, according to the findings of the experiments