Entropy-based deep neural network training optimization for optical coherence tomography imaging
This paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significantly decreases training epochs. Using a four-class Optical Coherence Tomography (OCT) image dataset encom...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2355760 |
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