Classification of Melanoma Images Using Modified Teaching Learning Based Artificial Bee Colony

Abstract

The great improvement in the current technology, particularly in the field of artificial intelligence, has effectively contributed to solving many problems, especially in the medical field. More recently, skin cancer (melanoma) has become one of the most dangerous cancers threatening human life, although it can be treated more frequently at early detection. Unfortunately, only highly-trained specialists can diagnose the disease accurately. Therefore, in this paper we have introduced various software technologies to detect and diagnose skin cancer through images, thus saving lives and reducing the spread of the disease, as well as reducing unnecessary traditional eradication of non-carcinogenic areas. Our method combines image processing techniques (image enhancement, hair removal and segmentation using Otsu's thresholding), feature extraction techniques (Gray Level Co-Occurrence Matrix (GLCM) features and color moments features) and commonly used classification methods, such as Weighted KNN, Cubic SVM, Medium Gaussian SVM, and Multi-Layer Perceptron (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 Learning Based Artificial Bee Colony (MTLABC) which is the proposed algorithm in this paper. Experimental results for 996 dermoscopy dataset images, show that the classification accuracy and the convergence of the trained Neural Network (NN) using the proposed MTLABC is better than the other evolutionary algorithms used in this study for the same purpose. At the same time, the experimental results show that the classification accuracy of the trained NN using the proposed MTLABC is better than the results of commonly used classification methods. 

Keywords

• TLABC
• Modified TLABC
• melanoma detection
• multi-layer perceptron
• classification methods
• metaheuristics

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