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
Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index
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.
2024
2024
42
51
10.54216/JISIoT.110204
https://www.americaspg.com/articleinfo/18/show/2431