An Efficient Cascade of U-Net-like Convolutional Neural Networks devoted to Brain Tumor Segmentation

Abstract: A glioma is a fast-growing and aggressive tumor that starts in the glial cells of the brain. They make up about 30% of all brain tumors, and 80% of all malignant brain tumors. Gliomas are considered to be rare tumors, affecting less than 10,000 people each year, with a 5-year survival rate of 6%. If intercepted at an early stage, they pose no danger; however, providing an accurate diagnosis has proven to be difficult. In this paper, we propose a cascade approach using state-of-the-art Convolutional Neural Networks, in order to maximize accuracy in tumor detection. Various U-Net-like networks have been implemented and tested in order to select the network best suited for this problem.

Recommended citation: Bouchet, P et al. (2023). "An Efficient Cascade of U-Net-like Convolutional Neural Networks devoted to Brain Tumor Segmentation." LNCS. 1(3).