##plugins.themes.academic_pro.article.sidebar##

Download
pdf
Statistic
Read Counter : 247 Download : 149

##plugins.themes.academic_pro.article.main##

Abstract

Approximately 75% of all cancers are found on the skin. Due to its high mortality rate, Skin cancer (SC) must be treated immediately after detection. SC, in fact, results from abnormalities in the skin's surface. In spite of the fact that most people who have SC make a full recovery, this disease remains a major source of anxiety for the general public. Most SCs develop only locally and invade surrounding tissues, but melanoma, the rarest form of SC, may move throughout the body via the bloodstream and lymphatic system. in this study k mean algorithm and color space has been used to detect melanoma skin cancer. The data set used has been obtained from Kaggle skin cancer collection challenge. The input image has been changed to color space, k mean clustering algorithm was used to cluster the image into three clusters and return an index corresponding to each cluster then the a’b’ layers have been used as image clusters and l layer has been used to detect the exact part of the image.

Keywords

skin image collection

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Wameedh Raad Fathel, Ahmed Saeed Ibrahim Al-Obaidi, Maysaloon Abed Qasim, & Marwa Mawfaq Mohamedsheet Al-Hatab2. (2023). Skin Cancer Detection Using K-Means Clustering-Based Color Segmentation. Texas Journal of Engineering and Technology, 18, 46–52. Retrieved from https://zienjournals.com/index.php/tjet/article/view/3615

References

  1. J. Kato, K. Horimoto, S. Sato, T. Minowa, and H. Uhara, “Dermoscopy of Melanoma and Non-melanoma Skin Cancers,” Frontiers in Medicine, vol. 6, no. August, pp. 1–7, 2019, doi: 10.3389/fmed.2019.00180.
  2. “Ultraviolet radiation.” https://www.who.int/news-room/fact-sheets/detail/ultraviolet-radiation (accessed Dec. 13, 2022).
  3. F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, 2018, doi: 10.3322/caac.21492.
  4. A. Di Stefani, I. Zalaudek, G. Argenziano, S. Chimenti, and H. P. Soyer, “Feasibility of a two-step teledermatologic approach for the management of patients with multiple pigmented skin lesions,” Dermatologic Surgery, vol. 33, no. 6, pp. 686–692, 2007, doi: 10.1111/j.1524-4725.2007.33144.x.
  5. I. Nindl, M. Gottschling, and E. Stockfleth, “Human papillomaviruses and non-melanoma skin cancer: Basic virology and clinical manifestations,” Disease Markers, vol. 23, no. 4, pp. 247–259, 2007, doi: 10.1155/2007/942650.
  6. V. Madan, J. T. Lear, and R. M. Szeimies, “Non-melanoma skin cancer,” The Lancet, vol. 375, no. 9715, pp. 673–685, 2010, doi: 10.1016/S0140-6736(09)61196-X.
  7. H. Tsao et al., “Early detection of melanoma: Reviewing the ABCDEs American Academy of Dermatology Ad Hoc Task Force for the ABCDEs of Melanoma,” Journal of the American Academy of Dermatology, vol. 72, no. 4, pp. 717–723, 2015, doi: 10.1016/j.jaad.2015.01.025.
  8. S. Jain, V. Jagtap, and N. Pise, “Computer aided melanoma skin cancer detection using image processing,” Procedia Computer Science, vol. 48, no. C, pp. 735–740, 2015, doi: 10.1016/j.procs.2015.04.209.
  9. R. Kasmi and K. Mokrani, “Classification of malignant melanoma and benign skin lesions: Implementation of automatic ABCD rule,” IET Image Processing, vol. 10, no. 6, pp. 448–455, 2016, doi: 10.1049/iet-ipr.2015.0385.
  10. T. Nagaoka, A. Nakamura, Y. Kiyohara, and T. Sota, “Melanoma screening system using hyperspectral imager attached to imaging fiberscope,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 30, pp. 3728–3731, 2012, doi: 10.1109/EMBC.2012.6346777.
  11. G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” Journal of Biomedical Optics, vol. 19, no. 1, p. 010901, 2014, doi: 10.1117/1.jbo.19.1.010901.
  12. M. Halicek, H. Fabelo, S. Ortega, G. M. Callico, and B. Fei, “In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: Revealing the invisible features of cancer,” Cancers, vol. 11, no. 6, pp. 1–30, 2019, doi: 10.3390/cancers11060756.
  13. . H. Johansen et al., “Recent advances in hyperspectral imaging for melanoma detection,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 12, no. 1, pp. 1–37, 2020, doi: 10.1002/wics.1465.
  14. A. Aljawawdeh, E. Imraiziq, and A. Aljawawdeh, “Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 8, no. 12, pp. 477–483, 2017.
  15. O. B, Roberta, P. Aledir S, and T. João Manuel R.S, “Computational diagnosis of skin lesions from dermoscopic images using combined features,” Neural Computing and Applications, vol. 10, pp. 6091–6111, 2019.
  16. B. Ahmad, M. Usama, C. M. Huang, K. Hwang, M. S. Hossain, and G. Muhammad, “Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network,” IEEE Access, vol. 8. pp. 39025–39033, 2020, doi: 10.1109/ACCESS.2020.2975198.
  17. A. S. R. Sinaga, “Color-based Segmentation of Batik Using the L*a*b Color Space,” SinkrOn, vol. 3, no. 2, p. 175, 2019, doi: 10.33395/sinkron.v3i2.10102.
  18. L. V. Ri and U. R. S. Xvlqj, “
  19. N. K. EL Abbadi and E. Saleem, “Automatic gray images colorization based on lab color space,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 3, pp. 1501–1509, 2020, doi: 10.11591/ijeecs.v18.i3.pp1501-1509.
  20. S. Saifullah, R. Dreżewski, A. Khaliduzzaman, L. Karlo Tolentino, and R. Ilyos, “K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 8, no. 2, pp. 175–185, 2022, doi: 10.26555/jiteki.v8i2.23724.
  21. H. Turi and B. Comp.(Hons), “Clustering-Based Colour Image Segmentation,” School of Computer Sciience and Software Engineering Monash University,Australia, vol. 4, no. 1, pp. 88–100, 2001.
  22. K. SCOTT MADER, “Skin Cancer MNIST: HAM10000” kaggle, 2019. [Online]. Available: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000, Accessed 12 November 2020.
  23. Marwa MohamedSheet AL-Hatab, Raid Rafi Omar Al-Nima, Ilaria Marcantoni, Camillo Porcaro, and Laura Burattini, “Classifying Various Brain Activities by Exploiting Deep Learning Techniques and Genetic Algorithm Fusion Method”, TEST Engineering & Management, Vol. 83, pp. 3035-3052, 2020.
  24. Marwa MohamedSheet AL-Hatab, Raid Rafi Omar Al-Nima, Ilaria Marcantoni, Camillo Porcaro, and Laura Burattini, “Comparison Study Between Three Axis Views of Vision, Motor and Pre-Frontal Brain Activities”, Journal of Critical Reviews, Vol. 7, Issue 5, pp. 2598-2607, 2020. doi:10.31838/jcr.07.05.430
  25. Marwa Mawfaq Mohamedsheet Al-Hatab, Raid Rafi Omar Al-Nima, Maysaloon Abed Qasim,''Classifying healthy and infected Covid-19 cases by employing CT scan image'', Bulletin of Electrical Engineering and Informatics, vol.11,Issue.6,pp. 3279-3287,2022.
  26. Marwa Mawfaq Mohamedsheet Al-Hatab, Mohammed Abid Alhashim, Maan Ahamed Fadhil, AL-Jadir Ali R Hasan, Tahani Ghanim Al-Sultan,'' Innovative Non-Invasive Blood Sugar Level Monitoring for Diabetes Using UWB Sensor'', Journal of Optoelectronics Laser, vol.41,Issue.4, pp. 422-437,2022.
  27. Marwa Mawfaq Mohamedsheet Al-Hatab, and Mohammad Zaid Shuaib AlNima. "Hematological Classification of White Blood Cells by Exploiting Digital Microscopic Images." Eurasian Research Bulletin , vol.18 , 2023