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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
TADOC : Tool for Automated Detection of Oral Cancer
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110364
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.
Abstract: Cancer is a group of related diseases and it is necessary to classify the type and its impact. In this paper an automated learning-based system for detection of oral cancer from Whole Slide Images (WSI) has been designed. The main challenges of the system were to handle the huge dataset and to train the machine learning model as it consumed more time for each iteration involved. This further increased the time consumed to get a proper model and decrease of freedom for experimentation. Other important key features of the system were to implement a futuristic deep learning architecture to classify small patches from the large whole slide images and use of carefully designed post-processing methods for the slide-based classification.
Keywords: Cancer; CT Scan; MRI Scan; Machine Learning; Deep learning; Convolutional Neural Network (CNN); Whole Slide Image (WSI); Residual Networks (ResNets)
Khalid Nazim Abdul Sattar, “TADOC : Tool for Automated Detection of Oral Cancer” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110364
@article{Sattar2020,
title = {TADOC : Tool for Automated Detection of Oral Cancer},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110364},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110364},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Khalid Nazim Abdul Sattar}
}
