Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index
Bashar Talib AL-Nuaimi1, Ruaa Azzah Suhail2, Sanaa adnan abbas3, El-Sayed M. El-kenawy4,*
1 Diyala University, College of Science, Department of Computer Science.
Directorate General of Education Diyala, Iraq
2 Diyala University, College of Science, Department of Computer Science.
Directorate General of Education Diyala, Iraq
3 Diyala University, College of Science, Department of Computer Science.
Directorate General of Education Diyala, Iraq
4 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology,Mansoura 35111, Egypt
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Abstract 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. |
Emails: alnuaimi_bashar@uodiyala.edu.iq; ruaaizzat@gmail.com; sanaalasadi2000@yahoo.com; skenawy@ieee.org
Received: August 07, 2023 Revised: November 19, 2023 Accepted: January 08, 2024
Keywords: Lung tumors; CT; COVID-19; DNN; CNN; VGG-16.