Cyclical Learning Rate Optimization on Deep Learning Model for Brain Tumor Segmentation

Cyclical Learning Rate Optimization on Deep Learning Model for Brain Tumor Segmentation

In recent years, deep learning has found widespread applications in tasks such as segmentation and classification. Fine-tuning hyperparameters is crucial to improve performance, with the learning rate being a key parameter. Various methods, including adaptive learning rates, learning rate scheduling...

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Bibliographic Details
Main Authors: Aziz Fajar, Riyanarto Sarno, Chastine Fatichah, Rahadian Indarto Susilo, Gusti Pangestu
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
3D image data
deep learning
exponential moving average
information and communication technology
learning rate
scientific research
Online Access:https://ieeexplore.ieee.org/document/10290895/

Internet

https://ieeexplore.ieee.org/document/10290895/

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