360 434
Full Length Article
Journal of Cognitive Human-Computer Interaction
Volume 5 , Issue 2, PP: 15-23 , 2023 | Cite this article as | XML | Html |PDF

Title

Effective lung cancer detection using deep learning network

  Vidyul Narayanan 1 * ,   Nithya P. 2 ,   Sathya M. 3

1  Department of Computer Science, Pondicherry University, India
    (vidyulnarayananps1@gmail.com)

2  Department of Computer Science, Pondicherry University, India
    (nithyapoupathy@gmail.com)

3  Department of Computer Science, Pondicherry University, India
    (satsubithra@gmail.com)

Full Text Download


Doi   :   https://doi.org/10.54216/JCHCI.050202

Received: December 18, 2022 Revised: March 18, 2023 Accepted: May 11, 2023

Abstract :

The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use of improved machine learning and processing of images. Images of lung disease from CT scan databases created using quasi cells are used for diagnosis. Multilayer illumination was used to analyse the generated images, which improved the precision of the lungs' depiction by probing each and every one of their pixels while simultaneously decreasing the amount of background noise. Lung CT images are pre-processed to remove noise, and then a more advanced deep learning network is used to isolate the affected region. The territory is partitioned into subnetworks according to the number of existing networks, from which different features are subsequently extracted. Next, an ensemble classifier should be used to correctly diagnose lung diseases. Using MATLAB simulation, the authors examine how the provided technique improves the rate at which lung cancer could be diagnosed.

Keywords :

Deep learning network; CT imaging; Multilayer illumination; quasi cells; MATLAB.

References :

[1] Hong Y, Hong SH, Oh YM et al (2018) Identification of lung cancer specific differentially methylated regions using genomewide DNA methylation study. Mol Cell Toxicol 14:315.

[2] Nair SS et al (2011) Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpGsequence coverage bias. Epigenetics 6:34–44.

[3] Gaudet F et al (2003) Induction of tumors in mice by genomic hypomethylation. Science 300:489–492.

[4] Tang M, Xu W, Wang Q, Xiao W, Xu R (2009) Potential of DNMT and its epigenetic regulation for lung cancer therapy. Curr Genomics 10:336–352

[5] Liu Z, Wang J, Yuan Z, Zhang B, Gong L, Zhao L, Wang P (2018) Preliminary results about application of intensity-modulated radiotherapy to reduce prophylactic radiation dose in limited-stage small cell lung cancer. J Cancer 9(15):2625–2630.

[6] Balmelli C, Railic N, Siano M, Feuerlein K, Cathomas R, Cristina V, Gu¨thner C, Zimmermann S, Weidner S, Pless M, Stenner F, Rothschild SI (2018) ‘‘Lenvatinib in advanced radioiodine-refractory thyroid cancer: a retrospective analysis of the swiss lenvatinib named patient program. J Cancer 9(2):250–255.

[7] Manser R, Lethaby A, Irving LB, Stone C, Byrnes G, Abramson MJ, Campbell D (2013) Screening for lung cancer. Cochrane Database of System Rev 6(6):CD001991.

[8] Brock MV et al (2008) DNA methylation markers and early recurrence in stage I lung cancer. N Engl J Med 358:1118–1128

[9] Wang CC et al (2015) HOXA5 inhibits metastasis via regulating cytoskeletal remodelling and associates with prolonged survival in non-small-cell lung carcinoma.

[10] Hulbert A, Jusue-Torres, I, Stark A, Chen C, Rodgers K, Lee B, Belinsky SA (2017) Early detection of lung cancer using DNA promoter hypermethylation in plasma and sputum. Clin Cancer Res 23(8):1998–2005.

[11] Nilaiswariya, R., J. Manikandan, and P. Hemalatha. "Improving Scalability And Security Medical Dataset Using Recurrent Neural Network And Blockchain Technology." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-6. IEEE, 2021.

[12] Sriram, S., J. Manikandan, P. Hemalatha, and G. Leema Roselin. "A Chatbot Mobile Quarantine App for Stress Relief." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-5. IEEE, 2021.

[13] Kiruba, K., P. Hemalatha, J. Manikandan, M. Madhin, and Raj S. Mohan. "Revolutionizing Secure Commercialization In Agriculture Using Blockchain Technology." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-6. IEEE, 2021.

[14] Hemalatha, P., J. Manikandan, G. Leemarosilin, and P. Kanimozhi. "SEA FOOD PROCESSING USING INTERNET OF THINGS AND CLOUD TECHNOLOGIES." PalArch's Journal of Archaeology of Egypt/Egyptology 17, no. 9 (2020): 5877-5885.

[15] Manikandan, J., Brahmadesam Viswanathan Krishna, and K. Surendar. "Sign Language Recognition using Machine Learning." In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1-5. IEEE, 2022.

[16] Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Humaniz Comput.

[17] Shakeel PM, Tobely TEE, Al-Feel H, Manogaran G, Baskar S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access 7:5577–5588

[18] Senthil Kumar K, Venkatalakshmi K, Karthikeyan K (2019) Lung cancer detection using image segmentation by means of various evolutionary algorithms. Comput Math Methods Med 2019:4909846.

[19] Song QZ, Zhao L, Luo XK, Dou XC (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 7:8314740.

[20] Venkatalakshmi K, Mercyshalinie S (2005) Classification of multispectral images using support vector machines based on PSO and k-means clustering. In: Proceedings of IEEE international conference on intelligent sensing and information processing, pp 127–133, Bangalore, India, Dec 2005

[21] Zhang X, Wang S (2012) Efficient data hiding with histogram preserving property. Telecommun Syst 49:179–185

[22] Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929

[23] Golmohammadi D, Creese RC, Valian H, Kolassa J (2009) Supplier selection based on a neural network model using genetic algorithm. IEEE Trans Neural Netw 20(9):1504–1519

[24] Tsai CW, Huang BC, Chiang MC (2014) A novel spiral optimization for clustering. In: Park J, Adeli H, Park N, Woungang I (eds) Mobile, ubiquitous, and intelligent computing. Lecture notes in electrical engineering, vol 274. Springer, Berlin

[25] Tamura K, Yasuda K (2011) Spiral multipoint search for global optimization. In: International conference on machine learning and applications, vol 1, pp 470–475

[26] Raoof K, Kamoona K, Budayan C (2019) Implementation of genetic algorithm integrated with the deep neural network for estimating at completion simulation. Adv Civ Eng. https://doi. org/10.1155/2019/7081073

[27] Kong Z, Li T, Luo J, Xu S (2019) Automatic tissue image segmentation based on image processing and deep learning. J Healthc Eng. https://doi.org/10.1155/2019/2912458

[28] Havaei M, Davy A, Warde-Farley D et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31.

[29] K. Nanagasabapathy; G. Harish; O. I. Allen Sebastian; N. Sowrabh Chandra; V. D. Ambeth Kumar,” Validation system using smartphone luminescence”, IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Pages: 235 – 239, 2017. DOI: 10.1109/ICICICT1.2017.8342566

[30] V.D.Ambeth Kumar et.al. , “A Survey on Face Recognition in Video Surveillance,” Lecturer Notes on Computational and Mechanism, Vol. 30, pp:

699-708, 2019 [31] V. D. Ambeth Kumaret.al , “ Cloud enabled media streaming using Amazon Web Services”, IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, Pages: 195 – 198, 2-4 Aug. 2017, India (DOI: 10.1109/icstm.2017.8089150)

Cite this Article as :
Style #
MLA Vidyul Narayanan, Nithya P., Sathya M.. "Effective lung cancer detection using deep learning network." Journal of Cognitive Human-Computer Interaction, Vol. 5, No. 2, 2023 ,PP. 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
APA Vidyul Narayanan, Nithya P., Sathya M.. (2023). Effective lung cancer detection using deep learning network. Journal of Journal of Cognitive Human-Computer Interaction, 5 ( 2 ), 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
Chicago Vidyul Narayanan, Nithya P., Sathya M.. "Effective lung cancer detection using deep learning network." Journal of Journal of Cognitive Human-Computer Interaction, 5 no. 2 (2023): 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
Harvard Vidyul Narayanan, Nithya P., Sathya M.. (2023). Effective lung cancer detection using deep learning network. Journal of Journal of Cognitive Human-Computer Interaction, 5 ( 2 ), 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
Vancouver Vidyul Narayanan, Nithya P., Sathya M.. Effective lung cancer detection using deep learning network. Journal of Journal of Cognitive Human-Computer Interaction, (2023); 5 ( 2 ): 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
IEEE Vidyul Narayanan, Nithya P., Sathya M., Effective lung cancer detection using deep learning network, Journal of Journal of Cognitive Human-Computer Interaction, Vol. 5 , No. 2 , (2023) : 15-23 (Doi   :  https://doi.org/10.54216/JCHCI.050202)
↑