TY - JOUR
T1 - Deep learning for size and microscope feature extraction and classification in Oral Cancer
T2 - enhanced convolution neural network
AU - Joshi, Prakrit
AU - Alsadoon, Omar Hisham
AU - Alsadoon, Abeer
AU - AlSallami, Nada
AU - Rashid, Tarik A.
AU - Prasad, P. W.C.
AU - Haddad, Sami
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/2
Y1 - 2023/2
N2 - Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting.
AB - Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting.
KW - Autoencoder
KW - Deep learning
KW - Feature extraction
KW - Images classification
KW - Information compression
KW - Oral Cancer
KW - Overfitting
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U2 - 10.1007/s11042-022-13412-y
DO - 10.1007/s11042-022-13412-y
M3 - Article
AN - SCOPUS:85135509657
SN - 1380-7501
VL - 82
SP - 6197
EP - 6220
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 4
ER -