{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/13b1acc3-3e1c-42b5-8a00-c834f3e1c3f0","name":"Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training","text":"# Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training\n\n**Authors:** Adwaitt Pandya, Ozioma C. Oguine, Harita Bhargava, Shrikant Zade\n**arXiv:** https://arxiv.org/abs/2605.04008v1\n**Published:** 2026-05-05T17:30:17Z\n\n## Abstract\nA brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant expresses growth, making it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors 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 dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.","keywords":["cs.CV","cs.LG"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}