Brain Tumor Segmentation with Efficient and Low-Complex Architecture Using RCNN and Modified U-Net

In medical applications, the boundless potential of image processing utilizing Deep Neural Networks has grabbed the interest of researchers. Brain tumor segmentation, which is a crucial piece of task, determines the location and extent of tumor areas. Numerous techniques for segmentation have been suggested by researchers. One significant disadvantage of the existing architectures is the presence of a large number of trainable parameters. It makes the system complex, expensive to train, and unsuitable for integration in low-powered devices. In this paper, we present an efficient, two-stage approach for the effective segmentation of brain tumor from MRI images using RCNN and a modified U-Net. The proposed system was evaluated and verified using a publicly available Figshare dataset (Cheng in, 2017 [1]). The system is low-complex with small number of parameters compared to other existing architectures. It was tested and compared to the original U-Net, and despite having a large decrease in total trainable parameters, it obtained a comparable performance with an accuracy of 99.78%, IoU of 89.76%, and a dice score of 94.53% in our experiments.