Simulation Study of Skin Tumors Detection Tool Using Artificial Intelligence (AI)

The objective of this research was to develop a system for early skin tumor diagnosis using artificial intelligence. With advancements in technology, early detection of skin tumors has become possible, and this study successfully achieved its objective. The chosen technology involves employing the Support Vector Machine (SVM) algorithm, along with image processing tools and artificial intelligence, resulting in a significantly improved accuracy rate of 85%. The software was developed using MATLAB. The methodology implemented in this research focuses on vertically segmenting skin tumor images obtained from the Kaggle platform, which provides diverse datasets. The proposed system aims to detect six distinct types of skin tumor diseases. To enhance detection accuracy, the research integrates the Gray Level Cooccurrence Matrix (GLCM) and Gabor Filter. These techniques contribute to developing a robust system structure. The classification of the six different diseases is based on analyzing input images. The research encompasses various stages, including preprocessing, segmentation, feature extraction, and the detection process. These steps collectively yield satisfactory results in addressing the challenges associated with skin tumor detection. Keywords: Skin, Tumors, Cancer, Detection, SVM, GLCM, Gabor Filter, MATLAB, AI.