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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

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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

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