Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2431 2019 2019 Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq El El-Sayed Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq Ruaa Azzah Suhail Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq Sanaa adnan abbas Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt El-Sayed M. El El-kenawy 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. 42 51 10.54216/JISIoT.110204 https://www.americaspg.com/articleinfo/18/show/2431