A Comparative Study on Lung Cancer Detection using Deep Learning Algorithms

International Journal of Computer Science and Engineering
© 2022 by SSRG - IJCSE Journal
Volume 9 Issue 5
Year of Publication : 2022
Authors : S. Farjana Farvin, S. Krishna Mohan

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How to Cite?

S. Farjana Farvin, S. Krishna Mohan, "A Comparative Study on Lung Cancer Detection using Deep Learning Algorithms," SSRG International Journal of Computer Science and Engineering , vol. 9,  no. 5, pp. 1-4, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I5P101

Abstract:

Because of its aggressive nature and late diagnosis at advanced stages, lung cancer is one of the major causes of cancer-related mortality. Early identification of lung cancer is critical for a person's survival, yet it is a difficult challenge to solve. Cancer identification is crucial for clinical and epidemiologic reasons since it helps to determine subsequent therapy. This paper reviews the performance of various deep-learning techniques in detecting lung cancer.

Keywords:

Convolution neural network, Machine learning, Deep learning, Recurrent neural network, Deep belief neural network.

References:

[1] Mehmet A. Gulum, Christopher M. Trombley, and Mehmed Kantardzic, “A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging,” Applied Science, vol. 11, no. 10, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Sunyi Zheng et al., “Automatic Pulmonary Nodule Detection in C.T. Scans using Convolutional Neural Networks Based on Maximum Intensity Projection,” IEEE Transactions on Medical Imaging, vol. 39, no. 3, pp. 797–805, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Qing Zeng Song et al., “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images,” Journal of Healthcare Engineering, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Dipanjan Moitra, and Rakesh Kr Mandal, “Automated AJCC (7th Edition) Staging of Non-Small Cell Lung Cancer (NSCLC) Using Deep Convolutional Neural Network (CNN) and Recurrent Neural Network (R.N.N),” Health Information Science and Systems, vol. 7, no. 1, pp. 14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Nasrullah Nasrullah et al., “Automated Lung Nodule Detection and Classification using Deep Learning Combined with Multiple Strategies,” Sensors, vol. 19, no. 17, pp. 3722, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] D. Lakshmi, K. Palani Thanaraj, and M. Arunmozhi, “Convolutional Neural Network in Detecting Lung Carcinoma Using Transfer Learning Approach,” International Journal of Imaging Systems and Technology, vol. 30, no. 2, pp. 445–454, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Nikitha Johnsirani Venkatesan, Dong Ryeol Shin, and Dong Ryeol Shin, “Nodule Detection with the Convolutional Neural Network using Apache Spark and GPU Frameworks,” Applied Science, vol. 11, no. 6, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Huseyin Polat, and Homay Danaei Mehr, “Classification of Pulmonary C.T. Images Using Hybrid 3d-Deep Convolutional Neural Network Architecture,” Applied Science, vol. 9, no. 5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Xinrong Lu, Y. A. Nanehkaran, and Maryam Karimi Fard, “A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm,” Computational Intelligence and Neuroscience, vol. 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Giang Son Tran et al., “Improving Accuracy of Lung Nodule Classification using Deep Learning with Focal Loss,” Journal of Healthcare Engineering, vol. 2019, 2019. [CrossRef] [Google Scholar] [Publisher Link]
[11] Mehedi Masud et al., “A Machine Learning Approach to Diagnosing Lung and Colon Cancer using Deep Learningā€ Based Classification Framework,” Sensors (Switzerland), vol. 21, no. 3, pp. 1–21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Michael Horry et al, “Deep Mining Generation of Lung Cancer Malignancy Models From Chest X-Ray Images,” Sensors, vol. 21, no. 19, pp. 1–23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Manoj Senthil Kailasam, and MeeraDevi Thiagarajan, “Detection of Lung Tumor using Dual-Tree Complex Wavelet Transform and Co-Active Adaptive Neuro-Fuzzy Inference System Classification Approach,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 2032–2046, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] O. Obulesu et al, “Adaptive Diagnosis of Lung Cancer by Deep Learning Classification using Wilcoxon Gain and Generator,” Journal of Healthcare Engineering, vol. 2021, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Diego Riquelme and Moulay Akhloufi, “Deep Learning for Lung Cancer Nodules Detection and Classification in C.T. Scans,” AI, vol. 1, no. 1, pp. 28–67, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Suren Makaju et al., “Lung Cancer Detection using C.T. Scan Images,” Procedia Computer Science, vol. 125, pp. 107–114, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Keerti Maithil, and Tasneem Bano Rehman, “Urban Remote Sensing Image Segmentation using Dense U-Net+,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 3, pp. 21-28, 2022.
[CrossRef] [Publisher Link]