Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
Adwaitt Pandya, Ozioma C. Oguine, Harita Bhargava, Shrikant Zade
TLDR
This paper enhances 3D brain tumor segmentation using SegResNet with automatic multi-precision training, achieving high Dice scores for early detection.
Key contributions
- Leveraged SegResNet architecture for state-of-the-art 3D brain tumor segmentation.
- Employed automatic multi-precision training to enhance model accuracy and efficiency.
- Achieved a high Dice score of 0.90 for whole tumor segmentation.
Why it matters
Early and accurate identification of brain tumors is critical for patient survival. This research offers a state-of-the-art computational method to significantly improve detection rates. By enhancing 3D segmentation, it provides a valuable tool for medical diagnosis.
Original Abstract
A 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.
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