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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Mahmoud Ragab1,2,3,*, Hesham A. Abdushkour4, Alaa F. Nahhas5, Wajdi H. Aljedaibi6

1 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2 Center of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
4 Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5 Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
6 Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

* Corresponding Author: Mahmoud Ragab. Email: email

Computers, Materials & Continua 2022, 73(1), 533-546. https://doi.org/10.32604/cmc.2022.028856

Abstract

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification (DHODCNN-LCC) model. The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal. Besides, the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model. In addition, denoising stacked autoencoder (DSAE) model is employed for lung nodule classification. Finally, the deer hunting optimization algorithm (DHOA) is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance. The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects. The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

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APA Style
Ragab, M., Abdushkour, H.A., Nahhas, A.F., Aljedaibi, W.H. (2022). Deer hunting optimization with deep learning model for lung cancer classification. Computers, Materials & Continua, 73(1), 533-546. https://doi.org/10.32604/cmc.2022.028856
Vancouver Style
Ragab M, Abdushkour HA, Nahhas AF, Aljedaibi WH. Deer hunting optimization with deep learning model for lung cancer classification. Comput Mater Contin. 2022;73(1):533-546 https://doi.org/10.32604/cmc.2022.028856
IEEE Style
M. Ragab, H.A. Abdushkour, A.F. Nahhas, and W.H. Aljedaibi "Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification," Comput. Mater. Contin., vol. 73, no. 1, pp. 533-546. 2022. https://doi.org/10.32604/cmc.2022.028856



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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