G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design

• Published in: PloS one Vol. 19; no. 8; p. e0308236
• Main Authors: D S, Chandra Sekaran, Clement J, Christopher
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.08.2024
• Subjects: Accuracy; Algorithms; Attention; Automation; Biology and Life Sciences; Brain; Brain architecture; Brain cancer; Brain mapping; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain research; Brain tumors; Classification; Computer vision; Datasets; Deep learning; Excitation; Feature maps; Glioma; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Localization; Machine learning; Machine vision; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical innovations; Medicine and Health Sciences; Neural networks; Neural Networks, Computer; Neuroimaging; Physical Sciences; Pictures; Research and Analysis Methods; Semantic segmentation; Semantics; Social Sciences; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0308236

A fundamental computer vision task called semantic segmentation has significant uses in the understanding of medical pictures, including the segmentation of tumors in the brain. The G-Shaped Net architecture appears in this context as an innovative and promising design that combines components from many models to attain improved accuracy and efficiency. In order to improve efficiency, the G-Shaped Net architecture synergistically incorporates four fundamental components: the Self-Attention, Squeeze Excitation, Fusion, and Spatial Pyramid Pooling block structures. These factors work together to improve the precision and effectiveness of brain tumor segmentation. Self-Attention, a crucial component of G-Shaped architecture, gives the model the ability to concentrate on the image's most informative areas, enabling accurate localization of tumor boundaries. By adjusting channel-wise feature maps, Squeeze Excitation completes this by improving the model's capacity to capture fine-grained information in the medical pictures. Since the G-Shaped model's Spatial Pyramid Pooling component provides multi-scale contextual information, the model is capable of handling tumors of various sizes and complexity levels. Additionally, the Fusion block architectures combine characteristics from many sources, enabling a thorough comprehension of the image and improving the segmentation outcomes. The G-Shaped Net architecture is an asset for medical imaging and diagnostics and represents a substantial development in semantic segmentation, which is needed more and more for accurate brain tumor segmentation.