Survey on Real Time-Detection of Lung Cancer

© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Mr K Arun Kumar, Nishanth S, Shankar Narayan K, Subikshan S
DOI :  10.14445/22312803/IJCTT-V68I4P106

How to Cite?

Mr K Arun Kumar, Nishanth S, Shankar Narayan K, Subikshan S, "Survey on Real Time-Detection of Lung Cancer," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 27-32, 2020. Crossref,

Lung infections are the most intense ailments that influence the lungs. Lung assumes a fundamental job which takes care in the breathing procedure in people. Lung ailments is said to be the most widely recognized ailments around the world, particularly in India it is increasingly normal. The regular maladies, for example, pleural emanation and typical lung can be recognized and grouped right now. This paper introduces a PC helped order Method in Computer Tomography (CT) Images of lungs created utilizing NN. The significant reason for this framework is to recognize and characterize the most widely recognized lung ailments that causes the significant issues by viable component extraction through Dual-Tree Complex Wavelet Transform and GLCM Features.Right now whole lung is fragmented from the CT Images and the parameters are determined from the divided picture. The parameters are determined utilizing GLCM. We Propose and assess the Network intended for grouping of ILD designs. The parameters gives the greatest grouping Accuracy. After outcome we propose the bunching to portion the injury part from irregular lung.

CT computed tomography, GLCM Gray-Level Co-Occurrence Matrix

[1] S. Sun, C. Bauer and R. Beichel, 2012,"Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach," in IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 449-460, Feb. 2012.
[2] R. Thriumani et al., 2014, "A preliminary study on in-vitro lung cancer detection using E-nose technology," 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), Batu Ferringhi, 2014, pp. 601-605,2014
[3] S. S. Kanitkar, N. D. Thombare and S. S. Lokhande, 2015, "Detection of lung cancer using marker-controlled watershed transform," 2015 International Conference on Pervasive Computing (ICPC), Pune, pp. 1-6, 2015.
[4] B. U. Dhaware and A. C. Pise, 2016, "Lung cancer detection using Bayasein classifier and FCM segmentation," 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), Pune, pp. 170-174, 2016.
[5] Q. Wu and W. Zhao, 2017 ,"Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm," International Symposium on Computer Science and Intelligent Controls (ISCSIC), Budapest, pp. 88-91, 2017.
[6] J. Alam, S. Alam and A. Hossan, 2018, "Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier," International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi , pp. 1-4, 2018.
[7] S. Hussein, P. Kandel, C. W. Bolan, M. B. Wallace and U. Bagci, 2019, "Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches," in IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1777-1787, Aug. 2019.
[8] Hsieh, Meng & Sun, Li-Min & Lin, Cheng-Li & Hsieh, Meng-Ju & Hsu, Chung & Kao, Chia-Hung. (2018). Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models. Cancer Management and Research. Volume 10, 2018.