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

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...

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Veröffentlicht in:	Multimedia tools and applications Jg. 83; H. 37; S. 85027 - 85046
Hauptverfasser:	Ali, Saqib, Khurram, Rooha, Rehman, Khalil ur, Yasin, Anaa, Shaukat, Zeeshan, Sakhawat, Zareen, Mujtaba, Ghulam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2024 Springer Nature B.V
Schlagworte:	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
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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