Open AccessSkin Cancer Diagnosis by Using Fuzzy Logic and GLCM
Author(s) -
Fatima Ghali
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1279/1/012020
Subject(s) - fuzzy logic , skin cancer , gray level , artificial intelligence , computer science , cancer detection , entropy (arrow of time) , pattern recognition (psychology) , homogeneity (statistics) , cancer , computer vision , machine learning , medicine , image (mathematics) , physics , quantum mechanics
Image processing is one of the most strong and popular computer science technologies increasingly used today especially with medical sciences. It’s commonly used to diagnose and detect many kinds of cancer diseases early such as skin cancer, and others. In this paper two techniques have been used to detect Skin Cancer. These two techniques are Fuzzy logic and GLCM (Gray Level Co-occurrence Matrix) where they can distinguish among cancerous skin and non-cancerous. The distinguish operation is based on extracted featured values from GLCM. The features GLCM include are Contrast, Correlation, Energy, Entropy, and Homogeneity. However, our contribution is a new algorithm for diagnosis two phase, the first is the normal situation and second is the skin cancer. After the design and implementation of the algorithm the result was good as we can see in the implementation section.