2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 11 , Issue 2 , PP: 42-51, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Bashar Talib AL-Nuaimi 1 , Ruaa Azzah Suhail 2 , Sanaa adnan abbas 3 , El-Sayed M. El-kenawy 4 *
Doi: https://doi.org/10.54216/JISIoT.110204
Early detection of Lung tumors, which is lethal and equally affects men and women, is challenging. In order to decrease mortality rates and raise survival rates, early detection and classification of Lung tumors is essential. However, at the start of 2020, the entire planet would be afflicted with a coronavirus that causes a fatal sickness (COVID-19). CT imaging is a good tool to detect illness among the various COVID-19 screening techniques available. On the other hand, alternative methods of disease detection take a lot of time. Deep learning, a type of machine learning, opens up a wealth of opportunities for investigating and assessing tumor features using CT scans, allowing for improved disease prediction, diagnosis, and classification. Using CNN, DNN, and VGG-16 models, the suggested approach in this research gives unambiguous and accurate categorization.
Lung tumors , CT , COVID-19 , DNN , CNN , VGG-16.
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