  Background and Aim: Over-fitting issue has been the reason behind deep
learning technology not being successfully implemented in oral cancer images
classification. The aims of this research were reducing overfitting for
accurately producing the required dimension reduction feature map through Deep
Learning algorithm using Convolutional Neural Network. Methodology: The
proposed system consists of Enhanced Convolutional Neural Network that uses an
autoencoder technique to increase the efficiency of the feature extraction
process and compresses information. In this technique, unpooling and
deconvolution is done to generate the input data to minimize the difference
between input and output data. Moreover, it extracts characteristic features
from the input data set to regenerate input data from those features by
learning a network to reduce overfitting. Results: Different accuracy and
processing time value is achieved while using different sample image group of
Confocal Laser Endomicroscopy (CLE) images. The results showed that the
proposed solution is better than the current system. Moreover, the proposed
system has improved the classification accuracy by 5~ 5.5% on average and
reduced the average processing time by 20 ~ 30 milliseconds. Conclusion: The
proposed system focuses on the accurate classification of oral cancer cells of
different anatomical locations from the CLE images. Finally, this study
enhances the accuracy and processing time using the autoencoder method that
solves the overfitting problem.
