{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/13b1acc3-3e1c-42b5-8a00-c834f3e1c3f0","identifier":"13b1acc3-3e1c-42b5-8a00-c834f3e1c3f0","url":"https://froggit.ai/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\nSource-backed public reference for arXiv:2605.04008.\n\n**Authors:** Adwaitt Pandya, Ozioma C. Oguine, Harita Bhargava, Shrikant Zade\n**Primary source:** https://arxiv.org/abs/2605.04008\n**Published:** 2026-05-05T17:30:17Z\n**Updated:** 2026-05-05T17:30:17Z\n**Categories:** cs.CV, cs.LG\n\n## Abstract Summary\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.\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat the paper as research context rather than medical, legal, safety, or engineering advice.\n\n## Source\n- https://arxiv.org/abs/2605.04008","keywords":["cs.CV","cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2605.04008"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-06T06:00:07.372000Z","dateModified":"2026-06-19T03:27:17Z","isBasedOn":"https://arxiv.org/abs/2605.04008","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}