دورية أكاديمية
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm
| العنوان: | Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm |
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| المؤلفون: | Araaf, Mamet Adil, Nugroho, Kristiawan, Setiadi, De Rosal Ignatius Moses |
| المصدر: | Journal of Computing Theories and Applications; Vol 1, No 1 (2023): JCTA 1(1) 2023; 31-40 ; 3024-9104 |
| بيانات النشر: | Universitas Dian Nuswantoro |
| سنة النشر: | 2023 |
| المجموعة: | Ruang Publikasi Ilmiah Universitas Dian Nuswantoro |
| مصطلحات موضوعية: | Comprehensive analysis of image recognition, Novel machine learning method, Image texture analysis, Skin disease detection, Skin disease recognition |
| الوصف: | Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://publikasi.dinus.ac.id/index.php/jcta/article/view/9185/4022Test; https://publikasi.dinus.ac.id/index.php/jcta/article/view/9185Test |
| DOI: | 10.33633/jcta.v1i1.9185 |
| الإتاحة: | https://doi.org/10.33633/jcta.v1i1.9185Test https://publikasi.dinus.ac.id/index.php/jcta/article/view/9185Test |
| حقوق: | Copyright (c) 2023 Mamet Adil Araaf, Kristiawan Nugroho, De Rosal Ignatius Moses Setiadi ; https://creativecommons.org/licenses/by-nc/4.0Test |
| رقم الانضمام: | edsbas.7D19AAD0 |
| قاعدة البيانات: | BASE |
| DOI: | 10.33633/jcta.v1i1.9185 |
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