EMAN RESEARCH PUBLISHING | <p>Ensemble Deep Learning based Lung Cancer Classification Model using Gene Expression Data</p>

Inflammation Cancer Angiogenesis Biology and Therapeutics | Impact 0.1 (CiteScore) | Online ISSN 2207-872X

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 8 (4)

Ensemble Deep Learning based Lung Cancer Classification Model using Gene Expression Data

V. Yuvaraj ^1*, D. Maheswari²

+ Author Affiliations

Journal of Angiotherapy 8 (4) 1-8 https://doi.org/10.25163/angiotherapy.849636

Submitted: 28 February 2024 Revised: 01 April 2024 Published: 04 April 2024

Abstract

Background: Globally, lung cancer is the deadliest form of the disease. Genetic variability is one of the elements that influence an individual's vulnerability to lung cancer, according to epidemiological research. Asian women, smokers or not, have a higher risk of acquiring cancer because of genetic abnormalities, according to a recent investigation from the US National Cancer Institute that involved 14,000 Asian women. A superior approach for classifying lung cancer was presented in recent studies to address the aforementioned issue. In this study, the data scale is first normalized utilizing min max normalization, which is accomplished by data pre-processing. Methods: Gene selection is carried through employing Improved Whale Optimization Algorithm (IWOA). An Enhanced Convolutional Neural Network (ECNN) is employed for lung cancer categorization. However, lung cancer classification using single algorithm produces insufficient accuracy. This required the need for development of ensemble models. To evade this issue,input data scales are normalized based on Z score normalization model. Once the normalization is done, significant genes are selected from these normalized gene samples using Modified Chicken Swarm Optimization (MCSO). Results: Finally, ensemble of ECNN, VGG16 and ResNet50 models are employed for lung cancer classification. Ensemble learning is performed in this work using majority voting. Conclusion: The suggested approach outperforms various alternatives in the field of accuracy, according to the findings.

Keywords: Lung cancer (LC), Classification, Gene Expression Data (GED), Z score normalization (ZSN), Chicken Swarm Optimization (CSO), Ensemble learning (EL) and majority voting (MV).

References

Albahar, M.A., (2019). Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access, 7, pp. 38306-38313. https://doi.org/10.1109/ACCESS.2019.2906241.

Al-Haija, Q.A. and Adebanjo, A., (2020). Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1-7. https://doi.org/10.1109/IEMTRONICS51293.2020.9216455.

Almugren, N. and Alshamlan, H., (2019). A survey on hybrid feature selection methods in microarray gene expression data for cancer classification. IEEE access, 7, pp. 78533-78548. https://doi.org/10.1109/ACCESS.2019.2922987.

Arunkumar, C. and Ramakrishnan, S., (2018). Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data. Future Computing and Informatics Journal, 3(1), pp. 131-142. https://doi.org/10.1016/j.fcij.2018.02.002.

Azzawi, H., Hou, J., Alanni, R. and Xiang, Y., (2019). A hybrid neural network approach for lung cancer classification with gene expression dataset and prior biological knowledge. In Machine Learning for Networking: First International Conference, Revised Selected Papers 1, pp. 279-293. https://doi.org/10.1007/978-3-030-19945-6_20.

Azzawi, H., Hou, J., Alanni, R., Xiang, Y., Abdu-Aljabar, R. and Azzawi, A., (2017). Multiclass lung cancer diagnosis by gene expression programming and microarray datasets. In Advanced Data Mining and Applications: 13th International Conference, ADMA, Proceedings 13, pp. 541-553. https://doi.org/10.1007/978-3-319-69179-4_38.

Azzawi, H., Hou, J., Alnnni, R. and Xiang, Y., (2018). SBC: a new strategy for multiclass lung cancer classification based on tumour structural information and microarray data. In IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), pp. 68-73. https://doi.org/10.1109/ICIS.2018.8466448.

Cahyaningrum, K. and Astuti, W., (2020). Microarray gene expression classification for cancer detection using artificial neural networks and genetic algorithm hybrid intelligence. In international conference on data science and its applications (ICoDSA), pp. 1-7. https://doi.org/10.1109/ICoDSA50139.2020.9213051.

Chaudhari, P. and Agarwal, H., (2018). Improving feature selection using elite breeding QPSO on gene data set for cancer classification. In Intelligent Engineering Informatics: Proceedings of the 6th International Conference on FICTA, pp. 209-219. https://doi.org/10.1007/978-981-10-7566-7_22.

Dass, M.V., Rasheed, M.A. and Ali, M.M., (2014). Classification of lung cancer subtypes by data mining technique. In Proceedings of the international conference on control, instrumentation, energy and communication (CIEC), pp. 558-562. https://doi.org/10.1109/CIEC.2014.6959151.

Diaz, J.M., Pinon, R.C. and Solano, G., (2014). Lung cancer classification using genetic algorithm to optimize prediction models. In IISA, The 5th International Conference on Information, Intelligence, Systems and Applications pp. 1-6. https://doi.org/10.1109/IISA.2014.6878770.

Hafez, A.I., Zawbaa, H.M., Emary, E., Mahmoud, H.A. and Hassanien, A.E., (2015). An innovative approach for feature selection based on chicken swarm optimization. In 7th international conference of soft computing and pattern recognition (SoCPaR), pp. 19-24. https://doi.org/10.1109/SOCPAR.2015.7492775.

Haznedar, B., Arslan, M.T. and Kalinli, A., (2021). Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data. Medical & Biological Engineering & Computing, 59, pp. 497-509. https://doi.org/10.1007/s11517-021-02331-z.

Hu, F., Zhou, Y., Wang, Q., Yang, Z., Shi, Y. and Chi, Q., (2019). Gene expression classification of lung adenocarcinoma into molecular subtypes. IEEE/ACM transactions on computational biology and bioinformatics, 17(4), pp. 1187-1197. https://doi.org/10.1109/TCBB.2019.2905553.

Jinthanasatian, P., Auephanwiriyakul, S. and Theera-Umpon, N., (2017). Microarray data classification using neuro-fuzzy classifier with firefly algorithm. In IEEE symposium series on computational intelligence (SSCI), pp. 1-6. https://doi.org/10.1109/SSCI.2017.8280967.

Kaur, T. and Gandhi, T.K., (2019). Automated brain image classification based on VGG-16 and transfer learning. In international conference on information technology (ICIT), pp. 94-98. https://doi.org/10.1109/ICIT48102.2019.00023.

Metwalli, A.S., Shen, W. and Wu, C.Q., (2020). Food image recognition based on densely connected convolutional neural networks. In international conference on artificial intelligence in information and communication (ICAIIC), pp. 027-032. https://doi.org/10.1109/ICAIIC48513.2020.9065281.

Moldovan, D., (2020). Cervical cancer diagnosis using a chicken swarm optimization based machine learning method. In international conference on e-health and bioengineering (EHB), pp. 1-4. https://doi.org/10.1109/EHB50910.2020.9280215.

Salem, H., Attiya, G. and El-Fishawy, N., (2017). Classification of human cancer diseases by gene expression profiles. Applied Soft Computing, 50, pp. 124-134. https://doi.org/10.1016/j.asoc.2016.11.026.

Tian, X. and Chen, C., (2019). Modulation pattern recognition based on Resnet50 neural network. In IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), pp. 34-38. https://doi.org/10.1109/ICICSP48821.2019.8958555.

Tripathi, A.K., Garg, P., Tripathy, A., Vats, N., Gupta, D. and Khanna, A., (2020). Prediction of cervical cancer using chicken swarm optimization. In International Conference on Innovative Computing and Communications: Proceedings of ICICC, 1, pp. 591-604. https://doi.org/10.1007/978-981-15-1286-5_51.

Venkatesan, C., Balamurugan, D., Thamaraimanalan, T. and Ramkumar, M., (2022). Efficient machine learning technique for tumor classification based on gene expression data. In 8th International Conference on Advanced Computing and Communication Systems (ICACCS), 1, pp. 1982-1986. https://doi.org/10.1109/ICACCS54159.2022.9785294.

Wang, Y., Li, X. and Ruiz, R., (2018). Weighted general group lasso for gene selection in cancer classification. IEEE transactions on cybernetics, 49(8), pp. 2860-2873. https://doi.org/10.1109/TCYB.2018.2829811.

Yuan, F., Lu, L. and Zou, Q., (2020). Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1866(8), pp. 165822. https://doi.org/10.1016/j.bbadis.2020.165822.

Zarlis, M., Yanto, I.T.R. and Hartama, D., (2016). A framework of training ANFIS using chicken swarm optimization for solving classification problems. In International conference on informatics and computing (ICIC), pp. 437-441. https://doi.org/10.1109/IAC.2016.7905759.

Abstract
Export Citation

View Dimensions

View Plumx

View Altmetric

Save

0
Citation

55
View

Share