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

Kaynakça

1. Kelly, J. W., Yeatman, J. M., Regalia, C., Mason, G., & Henham, A. P. (1997). A high incidence of melanoma found in patients with multiple dysplastic naevi by photographic surveillance. Medical journal of Australia, 167(4), 191-194.‏
2. LIVESCIENCE. https://www.livescience.com/34783-uv-rays-increase-melanoma-skin-cancer-risk.html. [Accessed 1 7 2019].
3. Jaiswar, S., Kadri, M., & Gatty, V. (2015). Skin Cancer Detection Using Digital Image Processing. International Journal of Scientific Engineering and Research, 3(6), 138-140.‏
4. Gopinathan, S., & Rani, S. N. A. (2016). The melanoma skin cancer detection and feature extraction through image processing techniques. Orthopedics, 5(11).‏
5. Al-Amin, M., Alam, M. B., & Mia, M. R. (2015). Detection of Cancerous and Non-cancerous Skin by using GLCM Matrix and Neural Network Classifier. International Journal of Computer Applications, 132(8), 44.‏
6. Ansari, U. B., & Sarode, T. (2017). Skin cancer detection using image processing. Int Res J Eng Technol, 4(4), 2875-2881.‏
7. Sheha, M. A., Mabrouk, M. S., & Sharawy, A. (2012). Automatic detection of melanoma skin cancer using texture analysis. International Journal of Computer Applications, 42(20), 22-26.‏
8. Sumithra, R., Suhil, M., & Guru, D. S. (2015). Segmentation and classification of skin lesions for disease diagnosis. Procedia Computer Science, 45, 76-85.‏

9. Rohra, J. G., Perumal, B., Narayanan, S. J., Thakur, P., & Bhatt, R. B. (2017). User localization in an indoor environment using fuzzy hybrid of particle swarm optimization & gravitational search algorithm with neural networks. In Proceedings of Sixth International Conference on Soft Computing for Problem Solving (pp. 286-295). Springer, Singapore.
10. Lien, L. C., & Cheng, M. Y. (2012). A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert Systems with Applications, 39(10), 9642-9650.‏
11. Chen, S., Liu, Y., Wei, L., & Guan, B. (2018). PS-FW: a hybrid algorithm based on particle swarm and fireworks for global optimization. Computational intelligence and neuroscience, 2018.‏
12. Tsai, P. W., Pan, J. S., Shi, P., & Liao, B. Y. (2011). A new framework for optimization based-on hybrid swarm intelligence. In Handbook of Swarm Intelligence (pp. 421-449). Springer, Berlin, Heidelberg.‏
13. Jadon, S. S., Tiwari, R., Sharma, H., & Bansal, J. C. (2017). Hybrid artificial bee colony algorithm with differential evolution. Applied Soft Computing, 58, 11-24.‏
14. Li, X., Peng, Z., Du, B., Guo, J., Xu, W., & Zhuang, K. (2017). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113, 10-26.‏
15. Chengli, F. A. N., Qiang, F. U., Guangzheng, L. O. N. G., & Qinghua, X. I. N. G. (2018). Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism. Journal of Systems Engineering and Electronics, 29(2), 405-414.‏
16. Chen, X., & Xu, B. (2018, June). Teaching-learning-based artificial bee colony. In International Conference on Swarm Intelligence (pp. 166-178). Springer, Cham.‏
17. Sharma, H., Bansal, J. C., & Arya, K. V. (2013). Opposition based lévy flight artificial bee colony. Memetic Computing, 5(3), 213-227. International Skin Imaging Collaboration.” Dataset images”. [Online]. Available: http://www.dermoscopy.org. [accessed 1 7 2019].
18. Cost, S., & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features. Machine learning, 10(1), 57-78.‏
19. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.‏