Enhanced 3D brain tumor segmentation using assortedprecision training

Abstract

A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spreadof non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, andsensory changes. This research explores two main categories of brain tumors: benign and malignant. Benignspreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucialfactor for the survival of patients. This research provides a state-of-the-art approach to the early identification oftumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the diceloss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got adice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.