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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.
Abstract: The most common and deadly cancers are lung and colon cancers. More than a quarter of all cancer cases are caused by them. Early detection of the disease, on the other hand, greatly raises the probability of survival. Image enhancement by Double CLAHE stages and modified neural networks are made to improve classification accuracy and use Deep Learning (DL) algorithms to automate cancer detection. A new Artificial Intelligent classification system is presented in this research to recognize five kinds of colon and lung tissues, three malignant and two benign, with three classes for lung cancer and two classes for colon cancer, based on histological images. The results of the study imply that the suggested system can accurately identify tissues of cancer up to 99.5%. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting different kinds of colon and lung tumors.
Nora yahia Ibrahim and Amira Samy Talaat, “An Enhancement Technique to Diagnose Colon and Lung Cancer by using Double CLAHE and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130833
@article{Ibrahim2022,
title = {An Enhancement Technique to Diagnose Colon and Lung Cancer by using Double CLAHE and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130833},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130833},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Nora yahia Ibrahim and Amira Samy Talaat}
}
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.