Character recognition using LeNet-5

Versione 1.0.1 (370 KB) da Amir Ebrahimi

A deep model (LeNet-5) trained on the MNIST dataset is used for character recognition.

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Aggiornato 6 mag 2021

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The LeNet-5 model implemented in this project has 3 convolutional layers and 2 fully-connected layers. It has 62,000 training parameters, and the image input size is 32*32. This model achieved 98.48% accuracy on the MNIST test set after training on its train set. MNIST is a dataset of handwritten digits with 70,000 centred fixed-size grey-scale images. More details about the dataset are available in:

http://yann.lecun.com/exdb/mnist

Run the GUI and select your image.

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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.

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Creato con R2020b

Compatibile con R2019b e release successive

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Ispirato da: Pre-trained 2D LeNet-5

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lenet5TLfun.mlx

Versione	Pubblicato	Note della release
1.0.1	6 mag 2021	The relevant paper is published.	Toolbox Zip
1.0.0	3 mar 2021		Toolbox Zip

MLA	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.
APA	Ebrahimi, A., Luo, S., & Alzheimer’s Disease Neuroimaging Initiative, for the. (2021). Convolutional neural networks for Alzheimer’s disease detection on MRI images. Journal of Medical Imaging, 8(02). SPIE-Intl Soc Optical Eng. Retrieved from https://doi.org/10.1117%2F1.jmi.8.2.024503
BibTeX	@article{Ebrahimi_2021, doi = {10.1117/1.jmi.8.2.024503}, url = {https://doi.org/10.1117%2F1.jmi.8.2.024503}, year = 2021, month = {apr}, publisher = {{SPIE}-Intl Soc Optical Eng}, volume = {8}, number = {02}, author = {Amir Ebrahimi and Suhuai Luo and for the Alzheimer's Disease Neuroimaging Initiative}, title = {Convolutional neural networks for Alzheimer's disease detection on {MRI} images}, journal = {Journal of Medical Imaging} }