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.

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}
}