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.
Citation pour cette source
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.
Remerciements
Inspiré par : Deep Learning Network Analyzer for Neural Network Toolbox, Pre-trained 2D LeNet-5
Informations générales
- Version 1.0.1 (277 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec les versions R2019b et ultérieures
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 1.0.1 | The relevant paper is published. |
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| 1.0.0 |
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