DOI: https://doi.org/10.14710/teknik.v44i2.44895

Detection of Actinic Keratosis Skin Cancer Using Gray Level Co-occurrence Matrix Texture Extraction and Color Extraction With Support Vector Machine Classification

Peningkatan Identifikasi Kanker Kulit Actinic Keratosis Menggunakan Kombinasi Sistem Ekstraksi dengan Klasifikasi Support Vector Machine

*Leonardus Sandy Ade Putra scopus  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Tanjungpura, Indonesia
Vincentius Abdi Gunawan scopus  -  Department of Electrical Engineering, Faculty of Engineering, University of Palangka Raya, Indonesia
Agus Sehatman Saragih  -  Department of Electrical Engineering, Faculty of Engineering, University of Palangka Raya, Indonesia
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Abstract

Nowadays, humans tend to carry out activities during the day, both indoors and outdoors. Activities carried out outdoors cause human skin to often receive direct exposure to sunlight, which contains ultraviolet (UV) rays. Direct exposure to UV rays on the skin will harm the skin's health, which is the covering of the human body. Harmful effects on the skin usually include the skin becoming dark and dull, burns, and even causes cancer. One of the skin cancers that may appear on human skin is Actinic Keratosis (AK) cancer. AK cancer is a type of cancer that is classified as benign and can be cured with medical help. However, if this cancer is not caught early, it can become Squamous Cell Carcinoma (SCC), a type of malignant cancer. This research aims to design a system for identifying AK cancer types using color and texture feature extraction. RGB color feature extraction is obtained from image color segmentation and RGB values. The Gray Level Co-occurrence Matrix (GLCM) method is used to determine the texture of the skin cancer. Identification is carried out by a classification process using a Support Vector Machine (SVM), which can recognize the type of AK cancer. This research uses three classification methods: classification with color extraction, classification with texture extraction, and classification with color and texture extraction. Research shows that the highest level of accuracy in cancer recognition reaches 96% by combining color and texture extraction results as classification determinants. So, the system designed has succeeded in recognizing the type of AK cancer early on..

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Keywords: actinic keratosis; skin cancer; segmentation; gray level co-occurrence matrix; support vector machine

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