Brain tumor segmentation and classification on MRI via deep hybrid representation learning

Detecting brain tumors plays an important role in patients’ lives as it can help specialists save them or let them succumb to a terminal illness otherwise. Magnetic Resonance Imaging (MRI) has been shown to be the most accurate method to detect tumors if any, as it can clearly project their existence to the output image. However, it can also result in less accurate evaluations when a human specialist is to evaluate the images. This is mostly due to fatigue, weak expertise, and insufficient amount of information in the image. The latter occurs if the tumor is not large enough to be detected in the images, or has overlapped with some brain regions that may deter the specialist from correctly identifying one as they are mistaken for the healthy brain region(s). Inspired to alleviate such less accurate diagnosis, this study is to propose a segmentation approach to aid specialists detect brain tumors. This approach can segment and classify brain tumors with 98.81 % pixel-level and 98.93 % classification accuracy on the MICCAI BraTS’20 benchmark dataset. The performance of the proposed method is the most accurate compared to previous studies.