Optimizing Hyperparameters of ResNet50 for 3t2FTS-based Tumor Grading

Three-dimensional (3D) MR imaging allows for a comprehensive assessment of crucial factors like brain tumor volume and location. Also, two-dimensional (2D) analysis comprises more methods than 3D-based analysis to be applied for tumor classification, e.g. high-grade glioma (HGG) vs low-grade glioma...

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Veröffentlicht in:	2024 8th International Conference on System Reliability and Safety (ICSRS) S. 656 - 661
Hauptverfasser:	Ocal, Aysun, Koyuncu, Hasan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 20.11.2024
Schlagworte:	Accuracy brain tumor Brain tumors Classification algorithms Computer architecture Convergence Data models discrete & continuous optimization feature transform Heuristic algorithms hyperparameter optimization image classification Optimization Residual neural networks Three-dimensional displays transfer learning
Online-Zugang:	Volltext
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