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Journal of Intelligent Systems and Internet of Things
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Title

Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index

  Bashar Talib AL-Nuaimi 1 ,   Ruaa Azzah Suhail 2 ,   Sanaa adnan abbas 3 ,   El-Sayed M. El-kenawy 4 *

1  Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq
    (alnuaimi_bashar@uodiyala.edu.iq)

2  Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq
    (ruaaizzat@gmail.com)

3  Diyala University, College of Science, Department of Computer Science, Directorate General of Education, Diyala, Iraq
    (sanaalasadi2000@yahoo.com)

4  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
    (skenawy@ieee.org)

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Doi   :   https://doi.org/10.54216/JISIoT.110204

Received: August 07, 2023 Revised: November 19, 2023 Accepted: January 08, 2024

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.

Keywords :

Lung tumors; CT; COVID-19; DNN; CNN; VGG-16.

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Cite this Article as :
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MLA Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy. "Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index." Journal of Intelligent Systems and Internet of Things, Vol. 11, No. 2, ,PP. 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
APA Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy. (). Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index. Journal of Journal of Intelligent Systems and Internet of Things, 11 ( 2 ), 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
Chicago Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy. "Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index." Journal of Journal of Intelligent Systems and Internet of Things, 11 no. 2 (): 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
Harvard Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy. (). Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index. Journal of Journal of Intelligent Systems and Internet of Things, 11 ( 2 ), 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
Vancouver Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy. Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index. Journal of Journal of Intelligent Systems and Internet of Things, (); 11 ( 2 ): 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
IEEE Bashar Talib AL-Nuaimi, Ruaa Azzah Suhail, Sanaa adnan abbas, El-Sayed M. El-kenawy, Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 11 , No. 2 , () : 42-51 (Doi   :  https://doi.org/10.54216/JISIoT.110204)
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