EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 465 (2023) E3S Web of Conf., 465 (2023) 02054 References
Open Access
Issue
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
Article Number 02054
Number of page(s) 5
Section Symposium on Electrical, Information Technology, and Industrial Engineering
DOI https://doi.org/10.1051/e3sconf/202346502054
Published online 18 December 2023
  1. W. Cruz-Knight and L. Blake-Gumbs, “Tuberculosis: An Overview,” Primary Care: Clinics in Office Practice, vol. 40, no. 3, pp. 743–756, 2013, doi: https://doi.org/10.1016/j.pop.2013.06.003. [CrossRef] [Google Scholar]
  2. WHO, Global Tuberculosis Report 2022. Geneva: World Health Organization, 2022. [Online]. Available: https://www.who.int/publications/i/item/97892400 61729 [Google Scholar]
  3. J. P. Zellweger, G. Sotgiu, M. Corradi, and P. Durando, “The diagnosis of latent tuberculosis infection (LTBI): currently available tests, future developments, and perspectives to eliminate tuberculosis (TB): The diagnosis of latent tuberculosis infection (LTBI),” La Medicina del Lavoro | Work, Environment and Health, vol. 111, no. 3, pp. 170–183, 2020. [Google Scholar]
  4. Q. H. Nguyen et al., “Deep Learning Models for Tuberculosis Detection from Chest X-ray Images,” in 2019 26th International Conference on Telecommunications (ICT), 2019, pp. 381–385. doi: 10.1109/ICT.2019.8798798. [Google Scholar]
  5. P. Chhikara, P. Singh, P. Gupta, and T. Bhatia, “Deep Convolutional Neural Network with Transfer Learning for Detecting Pneumonia on Chest X-Rays,” in Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals, 2020, pp. 155–168. [Google Scholar]
  6. A. P. Brady, “Error and discrepancy in radiology: inevitable or avoidable?” Insights into Imaging, vol. 8, 2017, doi: 10.1007/s13244-016-0534-1. [Google Scholar]
  7. X. Chen, “Image enhancement effect on the performance of convolutional neural networks,” Dissertation, p. 40, 2019. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:bth- 18523 [Google Scholar]
  8. T. Rahman et al., “Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization,” IEEE Access, vol. 8, pp. 191586–191601, 2020, doi: 10.1109/ACCESS.2020.3031384. [CrossRef] [Google Scholar]
  9. D. Das, K. C. Santosh, and U. Pal, “Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 3612–3619. doi: 10.1109/ICPR48806.2021.9412748. [Google Scholar]
  10. O. Faruk et al., “Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning,” J Healthc Eng, 2021, doi: 10.1155/2021/1002799. [Google Scholar]
  11. M. H. Ishtiaq et al., “Deep learning to predict Pulmonary Tuberculosis from Chest Poster Anterior Radiographs of Lungs,” in 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2022, pp. 1–5. doi: 10.1109/MACS56771.2022.10022769. [Google Scholar]
  12. Wahyono, “Analysis of the Effect of Image Enhancement on X-Ray Image-Based Detection of COVID-19,” Jurnal Teknologi Informasi, vol. 22, no. 1, pp. 186–194, 2023, [in Indonesian] doi: 10.33633/tc.v22i1.7195. [Google Scholar]
  13. K. Munadi, K. Muchtar, N. Maulina, and B. Pradhan, “Image Enhancement for Tuberculosis Detection Using Deep Learning,” IEEE Access, vol. 8, pp. 217897–217907, 2020, doi: 10.1109/ACCESS.2020.3041867. [CrossRef] [Google Scholar]
  14. A. K. Khuzani, M. Heidari, and S. A. Shariati, “COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images,” in Sci Rep, 2021, vol. 11, no. 9887. doi: 10.1038/s41598-021-88807-2. [Google Scholar]
  15. S. M. Fati, E. M. Senan, and N. ElHakim, “Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion,” Applied Sciences, vol. 12, no. 14, 2022, doi: 10.3390/app12147092. [Google Scholar]
  16. R. C. Gonzalez and R. E. Woods, Digital Image Processing (4th Edition). Prentice-Hall, Inc., 2014. [Google Scholar]
  17. K. Zuiderveld, “Contrast Limited Adaptive Histogram Equalization,” in Graphics Gems IV, USA: Academic Press Professional, Inc., 1994, pp. 474–485. [CrossRef] [Google Scholar]
  18. A. M. Reza, “Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement,” The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, vol. 38, 2004, doi: 10.1023/B: VLSI.0000028532.53893.82. [Google Scholar]
  19. R. N. Bracewell, The Fourier Transform and Its Applications 3rd Edition. New York: McGraw-Hill, 1999. [Google Scholar]
  20. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. The MIT Press, 2016. [Online]. Available:https://mitpress.mit.edu/9780262035613 /deep-learning/ [Google Scholar]
  21. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision. arXiv, 2015. doi: 10.48550/ARXIV.1512.00567 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.