Pre-trained 3D LeNet-5
Pre-trained Neural Network Toolbox Model for 3D LeNet-5 Network
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Our implementation of 2D LeNet-5 model achieved 98.48% accuracy on the grey-scale MNIST test set after training on its train set. To transfer the learnable parameters from pre-trained 2D LeNet-5 (MNIST) to 3D one, we duplicated 2D filters (copying them repeatedly) through the third dimension. This is possible since a video or a 3D image can be converted into a sequence of image slices. In the training process, we expect that the 3D LeNet-5 learns patterns in each frame. This model has about 260,000 learnable parameters.
simply, call "lenet5TL3Dfun()" function.
Cite As
Ebrahimi, Amir, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021, doi:10.1117/1.jmi.8.2.024503.
Acknowledgements
Inspired by: Deep Learning Network Analyzer for Neural Network Toolbox, Pre-trained 2D LeNet-5
General Information
- Version 1.0.1 (277 KB)
MATLAB Release Compatibility
- Compatible with R2019b and later releases
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
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| 1.0.1 | The relevant paper is published. |
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| 1.0.0 |
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