The Detection of Lung Tumors Using CT scan Images with Feature Extraction and Segmentation Techniques.
Keywords:
Artificial Intelligence, pattern recognition, Segmentation, Medical Images, K-means, Edge-Based, Thresholding, Feature extractionAbstract
In this paper, we present a comparative study of segmentation and feature extraction techniques for lung cancer detection. Various segmentation techniques, including Thresholding, global Thresholding, and watershed segmentation, are performed and evaluated. Additionally, feature extraction is conducted to further enhance the performance of segmentation techniques. The proposed approach is compared with existing techniques to highlight its effectiveness and potential for improved lung tumor detection. In this research work, we analyse two tables: the first presents textual-based results, and the second provides statistical values. Unlike the existing system that employs only one technique, we apply different techniques on 5 images for analysis. The results demonstrate that the proposed segmentation and feature extraction techniques can achieve higher accuracy and potentially aid in early lung cancer detection.
Downloads
References
Bharati S, Podder P, Mondal R, Mahmood A, Raihan-Al-Masud M. Comparative Performance Analysis of Different Classification Algorithm for the Purpose of Prediction of Lung Cancer. In: International Conference on Intelligent Systems Design and Applications. Vellore, India: Springer (2018). p. 447–57.
Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and Mutation Prediction From non–Small Cell Lung Cancer Histopathology Images Using Deep learning. Nat Med (2018) 24:1559–67. doi: 10.1038/s41591-018-0177-5
Nie L, Wang M, Zhang L, Yan S, Zhang B, Chua TS. Disease Inference from Health-Related Questions via Sparse Deep Learning. In: IEEE Transactions on Knowledge and Data Engineering (2015). p. 2107–19.
Smys, S., and Joy Iong-Zong Chen. "Special Section on Innovative Engineering Solutions for Future Health Care Informatics." Journal of Medical Imaging and Health Informatics 6, no. 7, pp. 1570-1571, 2016
Chandy, A., “A REVIEW on IOT based medical imaging technology for healthcare applications”, Journal of Innovative Image Processing (JIIP), 1(01), pp. 51-60, 2019.
Gajanand Gupta, “Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-5, November 2011
H. Mahersia, M.Zaroug and Gabrella, “Lung Cancer Detection on CT Images: A Review on the Analysis Techniques “International Journal of Advanced Research in Artificial Intelligence, Vol.4, No.4,2015
S. Diederich, M. Thomas, M. Semik, H. Lenzen, N. Roos, A. Weber, W. Heindel, and D. Wormanns, “Screening for early lung cancer with low-dose spiral computed tomography: results of annual follow-up examinations in asymptomatic smokers,” Eur. Radiol. 14 (4), 691–702.
Rathod, S., Ragha, L.: Using Weakly Supervised Machine learning Algorithms for Classification and Analysis of CT Scan Lung Cancer Images (2022)
Erdi, E. Yusuf, et al. “Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding”, Cancer, 80.S12, pp. 2505-2509, 1997.
S. Xie, Z. Yu, and Z. Lv, “Multi-disease prediction based on deep learning: a survey,” Computer Modeling in Engineering & Sciences, vol. 128, no. 2, pp. 489–522, 2021.
T. Higaki, Y. Nakamura, J. Zhou et al., “Deep learning reconstruction at CT: phantom study of the image characteristics,” Academic Radiology, vol. 27, no. 1, pp. 82–87, 2020.
X. P. Wang, B. Wang, P. Hou, and J. B. Gao, “Screening and comparison of polychromatic and monochromatic image reconstruction of abdominal arterial energy spectrum CT,” Journal of Biological Regulators & Homeostatic Agents, vol. 31, no. 1, pp. 189–194, 2017.
Z. Wang, Y. Ni, Y. Zhang, Q. Xia, and H. Wang, “Laparoscopic varicocelectomy: virtual reality training and learning curve,” Journal of the Society of Laparoendoscopic Surgeons Journal of the Society of Laparoendoscopic Surgeons, vol. 18, no. 3, Article ID e2014.00258, 2014.
R. Nair, S. Vishwakarma, M. Soni, T. Patel, and S. Joshi, “Detection of COVID-19 cases through X-ray images using hybrid deep neural network,” World Journal of Engineering, vol. 19, no. 1, pp. 33–39, 2021.
T. Sharma, R. Nair, and S. Gomathi, “Breast cancer image classification using transfer learning and convolutional neural network,” IJMORE, vol. 2, no. 1, pp. 8–16, 2022
J. Kuruvilla and K. Gunavathi, “Lung cancer classification using neural networks for CT images,” Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 202–209, 2014.
M. Nasser and S. S. Abu-Naser, “Lung cancer detection using artificial neural network,” International Journal of Engineering and Information Systems (IJEAIS), vol. 3, no. 3, pp. 17–23, 2019.
S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: a survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1, 2021.
G. Zhang, G. Kang, Y. Wei, and Y. Yang, “Few-shot segmentation via cycle-consistent transformer,” 2021, arXiv preprint arXiv:2106.02320.
H. Tang, X. Qi, D. Xu, P. H. Torr, and N. Sebe, “Edge guided GANs with semantic preserving for semantic image synthesis,” 2020, arXiv preprint arXiv:2003.13898.
S. M. Naqi, M. Sharif, and A. Jaffar, “Lung nodule detection and classification based on geometric fit in parametric form and deep learning,” Neural Computing and Applications, vol. 32, no. 9, pp. 4629–4647, 2020.
G. Savitha and P. Jidesh,“A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans,” in Computers & Electrical Engineering, vol. 84, article 106626, Elsevier, New York, United States, 2020.
Lung Cancer Staging Essentials, available at http://pubs.rsna.org/doi/full/10.1148/rg.305095166
Shiying Hu, EricA Huffman, and Jospe h M. Reinhard t," Automatic lung segmentation for accurate quantitation of volumetric X-Ra CT images", IEEE Transactions on Medical Imaging, vol. 20, no. 6 pp. 490 -498, June 2001.
P. Soille, "Morphological Image Analysis - Principles and Applications", Springer-Verlag, 1999.
Rathod, S., Ragha, L.: Analysis of CT Scan Lung Cancer Images using Machine Learning Algorithms (2022)
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
