Akademik Veri Yönetim Sistemi
Avrupa Bilim ve Teknoloji Dergisi, cilt.0, sa.0, ss.225-232, 2019 (Hakemli Dergi)
The great improvement in the current technology, particularly in the field of artificial intelligence, has effectively contributed to solvingmany problems, especially in the medical field. More recently, skin cancer (melanoma) has become one of the most dangerous cancersthreatening human life, although it can be treated more frequently at early detection. Unfortunately, only highly-trained specialists candiagnose the disease accurately. Therefore, in this paper we have introduced various software technologies to detect and diagnose skincancer through images, thus saving lives and reducing the spread of the disease, as well as reducing unnecessary traditional eradicationof non-carcinogenic areas. Our method combines image processing techniques (image enhancement, hair removal and segmentationusing Otsu's thresholding), feature extraction techniques (Gray Level Co-Occurrence Matrix (GLCM) features and color momentsfeatures) and commonly used classification methods, such as Weighted KNN, Cubic SVM, Medium Gaussian SVM, and Multi-LayerPerceptron (MLP) trained by some of the common swarm intelligent techniques like Artificial Bee Colony (ABC), Genetic Algorithm(GA), Particle Swarm Optimization (PSO), Teaching Learning Based Artificial Bee Colony (TLABC), and Modified Teaching LearningBased Artificial Bee Colony (MTLABC) which is the proposed algorithm in this paper. Experimental results for 996 dermoscopy datasetimages, show that the classification accuracy and the convergence of the trained Neural Network (NN) using the proposed MTLABCis better than the other evolutionary algorithms used in this study for the same purpose. At the same time, the experimental results showthat the classification accuracy of the trained NN using the proposed MTLABC is better than the results of commonly used classificationmethods.