An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI

• Published in: Multimedia tools and applications Vol. 83; no. 37; pp. 85027 - 85046
• Main Authors: Ali, Saqib, Khurram, Rooha, Rehman, Khalil ur, Yasin, Anaa, Shaukat, Zeeshan, Sakhawat, Zareen, Mujtaba, Ghulam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Brain; Brain cancer; Comparative studies; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Deep learning; Encoders-Decoders; Image enhancement; Image segmentation; Machine learning; Magnetic resonance imaging; Medical prognosis; Multimedia Information Systems; Neural networks; Performance evaluation; Special Purpose and Application-Based Systems; Three dimensional models; Track 8: Medical Imaging; Transition layers; Tumors; Brain tumor diagnosis; Deep learning; Multi-modal MRI; Computer-aid diagnosis; Tumor segmentation

Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors...