Detection of Lung and Colon Cancer using Average and Weighted Average Ensemble Models

Authors

  • Hemalatha Gunasekaran University of Technology and Applied Sciences
  • S Deepa Kanmani Sri Krishna College of Engineering and Technology
  • Shamila Ebenezer Karunya University image/svg+xml
  • Wilfred Blessing University of Technology and Applied Sciences
  • K Ramalakshmi Alliance University image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.10.5017

Keywords:

Ensemble models, Weighted average ensemble method, Average Ensemble, Transfer Learning Approach, Lung and Colon Cancer

Abstract

INTRODUCTION: Cancer is a life-threatening condition triggered by metabolic irregularities or the convergence of hereditary disorders. Cancerous cells in lung and colon leads more death rate count in the human race today. The histological diagnosis of malignant cancers is critical in establishing the most appropriate treatment for patients. Detecting cancer in its early stages, before it has a chance to advance within the body, greatly reduces the risk of death in both cases.

OBJECTIVES: In order to examine a larger patient group more efficiently and quickly, researchers can utilize different methods of machine learning approach and different models of deep learning used to speed up the detection of cancer.

METHODS: In this work, we provide a new ensemble transfer learning model for the rapid detection of lung and colon cancer. By ingtegrating various models of transfer learning approach and combining these methods in an ensemble, we aim to enhance the overall performance of the diagnosis process.

RESULTS: The outcomes of this research indicate that our suggested approach performs better than current models, making it a valuable tool for clinics to support medical personnel in more efficiently detecting lung and colon cancer.

CONCLUSION: The average ensemble is able to reach an accuracy of 98.66%, while the weighted-average ensemble with an accuracy of 99.80%, which is good with analysis of existing approaches.

Downloads

Download data is not yet available.

References

Goossens, N, Nakagawa, S, Sun, X, Hoshida, Y. Cancer biomarker discovery and validation. Translational cancer research. 2015; 4(3): 256.

Fass, Leonard. Imaging and cancer: a review. Molecular oncology 2.2. 2008; 115-152. DOI: https://doi.org/10.1016/j.molonc.2008.04.001

Mehmood, Shahid, et al. Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access. 2022: 10 (2022): 25657-25668.

Demir, Fatih. DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. Applied soft computing. 2021;103 :107160. DOI: https://doi.org/10.1016/j.asoc.2021.107160

Das, Sumit, S. Biswas, Aditi Paul, and Aritra Dey. AI Doctor: An intelligent approach for medical diagnosis. In Industry Interactive Innovations in Science, Engineering and Technology: Proceedings of the International Conference, Singapore.Springer;2018 pp. 173-183. DOI: https://doi.org/10.1007/978-981-10-3953-9_17

Turkoglu, Muammer. COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Applied Intelligence. 2021;51(3): 1213-1226. DOI: https://doi.org/10.1007/s10489-020-01888-w

Tumen, Vedat, Ozal Yildirim, and Burhan Ergen. Recognition of road type and quality for advanced driver assistance systems with deep learning. Elektronika ir Elektrotechnika .2018;24(6): 67-74. DOI: https://doi.org/10.5755/j01.eie.24.6.22293

Talukder MA, Islam MM, Uddin MA, Akhter A, Hasan KF, Moni MA. Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications. 2022; Nov 1(205):117695. DOI: https://doi.org/10.1016/j.eswa.2022.117695

Mehmood S, Ghazal TM, Khan MA, Zubair M, Naseem MT, Faiz T, Ahmad M. Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access. 2022; Feb 10(10):25657-68. DOI: https://doi.org/10.1109/ACCESS.2022.3150924

Hage Chehade A, Abdallah N, Marion JM, Oueidat M, Chauvet P. Lung and colon cancer classification using medical imaging: A feature engineering approach. Physical and Engineering Sciences in Medicine. 2022 Sep;45(3):729-46. DOI: https://doi.org/10.1007/s13246-022-01139-x

Mohamed AA, Hançerlioğullari A, Rahebi J, Ray MK, Roy S. Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm. Diagnostics. 2023;13(10):1728. DOI: https://doi.org/10.3390/diagnostics13101728

Mengash HA, Alamgeer M, Maashi M, Othman M, Hamza MA, Ibrahim SS, Zamani AS, Yaseen I. Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis. Cancers. 2023; 15(5):1591. DOI: https://doi.org/10.3390/cancers15051591

Abdullah DM, Abdulazeez AM, Sallow AB. Lung cancer prediction and classification based on correlation selection method using machine learning techniques. Qubahan Academic Journal. 2021;1(2):141-9. DOI: https://doi.org/10.48161/qaj.v1n2a58

Gunasekaran H, Ramalakshmi K, Swaminathan DK, Mazzara M. GIT-Net: an ensemble deep learning-based GI tract classification of endoscopic images. Bioengineering. 2023 ;10(7):809. DOI: https://doi.org/10.3390/bioengineering10070809

Shanbhag GA, Prabhu KA, Reddy NS, Rao BA. Prediction of lung cancer using ensemble classifiers. In Journal of Physics. Proceedings of International Conference on Artificial Intelligence, Computational Electronics and Communication System; 28-30 October 2021; Manipal. IOP Publishing; 2021. P012007.

Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T. Transfer learning for medical image classification: a literature review. BMC medical imaging. 2022;22(1):69. DOI: https://doi.org/10.1186/s12880-022-00793-7

Raghu M, Zhang C, Kleinberg J, Bengio S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems. 2019;32.

Albashish D. Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images. PeerJ Computer Science. 2022 Jul 5(8): e1031. DOI: https://doi.org/10.7717/peerj-cs.1031

Gunasekaran H, Ramalakshmi K, Rex Macedo Arokiaraj A, Deepa Kanmani S, Venkatesan C, Suresh Gnana Dhas C. Analysis of DNA sequence classification using CNN and hybrid models. Computational and Mathematical Methods in Medicine. 2021 ;Jul (15):2021. DOI: https://doi.org/10.1155/2021/1835056

L. T. Omar, J. M. Hussein, L. F. Omer, A. M. Qadir and M. I. Ghareb. Lung and Colon Cancer Detection Using Weighted Average Ensemble Transfer Learning; 11th International Symposium on Digital Forensics and Security (ISDFS); Chattanooga, TN, USA: IEEE; 2023 pp. 1-7. DOI: https://doi.org/10.1109/ISDFS58141.2023.10131836

Downloads

Published

05-02-2024

How to Cite

1.
Gunasekaran H, Kanmani SD, Ebenezer S, Blessing W, Ramalakshmi K. Detection of Lung and Colon Cancer using Average and Weighted Average Ensemble Models. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Feb. 5 [cited 2024 Oct. 14];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5017