Skin Lesion Classification using Machine Learning Algorithms



Melanoma is a deadly skin cancer that breaks out in the skin’s pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.


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A. M. Y. Palomo, M. d. J. D. Pérez, O. R. P. Pérez, V. d. J. A. Yabor, and A. M. Fontaine, "Melanoma maligno cutáneo en pacientes de la provincia de Las Tunas," Revista Electrónica Dr. Zoilo E. Marinello Vidaurreta, vol. 40, 2015.

R. o. T. M. o. Health, "Türkiye Melanom Yol Haritası," 2012.

T. I. C. A. Board, "The melanoma white paper: Reshaping EU healthcare for melanoma patients.," 2012.

A. C. Society, "Cancer Facts & Figures," 2016.

M. A. Sedat Özçelik, "Epidemiology of Melanoma," TURKDERM, vol. 41, pp. 1-5, 2007.

B. ÖZTÜRK, E. YAMAN, K. Ali Osman, R. YILDIZ, U. DEMİRCİ, U. COŞKUN, et al., "Kutanöz malign melanomda adjuvan medikal tedavi yaklaşımları," Türk Onkoloji Dergisi, vol. 25, pp. 170-80, 2010.

H. Yalcin, "Çeşitli Özniteliklerle Kötü Huylu Melanom Karakterizasyonu Characterization of Melanomas Using a Variety of Features," Vogue, vol. 15, 2015.

G. Argenziano, G. Fabbrocini, P. Carli, V. De Giorgi, E. Sammarco, and M. Delfino, "Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis," Archives of dermatology, vol. 134, pp. 1563-1570, 1998.

R. H. Johr, "Dermoscopy: alternative melanocytic algorithms—the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist," Clinics in dermatology, vol. 20, pp. 240-247, 2002.

N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith, "Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images," in International Workshop on Machine Learning in Medical Imaging, 2015, pp. 118-126.

Z. Ma and J. M. R. Tavares, "A novel approach to segment skin lesions in dermoscopic images based on a deformable model," IEEE journal of biomedical and health informatics, vol. 20, pp. 615-623, 2016.

J. Premaladha and K. Ravichandran, "Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms," Journal of medical systems, vol. 40, p. 96, 2016.

J. C. Boldrick, C. J. Layton, J. Nguyen, and S. M. Swetter, "Evaluation of digital dermoscopy in a pigmented lesion clinic: Clinician versus computer assessment of malignancy risk," Journal of the American Academy of Dermatology, vol. 56, pp. 417-421, 2007/03/01/ 2007.

M. Aboras, H. Amasha, and I. Ibraheem, "Early detection of melanoma using multispectral imaging and artificial intelligence techniques," American Journal of Biomedical and Life Sciences, vol. 3, pp. 29-33, 2015.

E. Albay and M. Kamaşak, "Skin lesion classification using fourier descriptors of lesion borders," in Medical Technologies National Conference (TIPTEKNO), 2015, 2015, pp. 1-4.

T. Mendonça, P. M. Ferreira, J. S. Marques, A. R. Marcal, and J. Rozeira, "PH 2-A dermoscopic image database for research and benchmarking," in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 2013, pp. 5437-5440.

U. Fidan, İ. Sarı and R. K. Kumrular, "Classification of skin lesions using ANN," in Medical Technologies National Congress (TIPTEKNO) 2016, 2016, pp. 1-4.

A. Baştürk, M. E. Yüksel, H. Badem and A. Çalışkan, "Deep neural network based diagnosis system for melanoma skin cancer," in Signal Processing and Communications Applications Conference (SIU), 2017 25th, 2017, pp. 1-4.

R. J. Schalkoff, Artificial neural networks vol. 1: McGraw-Hill New York, 1997.

C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification," 2003.

T. Mitchell, "Machine Learning, McGraw-Hill Higher Education," New York, 1997.

S. R. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE transactions on systems, man, and cybernetics, vol. 21, pp. 660-674, 1991.

W. Dai and W. Ji, "A mapreduce implementation of C4. 5 decision tree algorithm," International journal of database theory and application, vol. 7, pp. 49-60, 2014.

V. N. Vapnik and V. Vapnik, Statistical learning theory vol. 1: Wiley New York, 1998.

K. H. Brodersen, C. S. Ong, K. E. Stephan, and J. M. Buhmann, "The balanced accuracy and its posterior distribution," in Pattern recognition (ICPR), 2010 20th international conference on, 2010, pp. 3121-3124.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software," Pacific California, 1984.

D. Thukaram, H. Khincha, and H. Vijaynarasimha, "Artificial neural network and support vector machine approach for locating faults in radial distribution systems," IEEE Transactions on Power Delivery, vol. 20, pp. 710-721, 2005.

I. The MathWorks, "MATLAB and Neural Network Toolbox Release ", ed: Natick, Massachusetts, United States., 2016.

I. The MathWorks, "MATLAB Statistics and Machine Learning Toolbox," ed: Natick, Massachusetts, United States., 2016.

M. F. Møller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural networks, vol. 6, pp. 525-533, 1993.

S. Jain and N. Pise, "Computer aided melanoma skin cancer detection using image processing," Procedia Computer Science, vol. 48, pp. 735-740, 2015.

S. Turkeli, M. S. Oguz, S. B. Abay, T. Kumbasar, H. T. Atay, and K. K. Kurt, "A smart dermoscope design using artificial neural network," in Artificial Intelligence and Data Processing Symposium (IDAP), 2017 International, 2017, pp. 1-6.

L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, "Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification," in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, pp. 1055-1058.

J. S. Marques, C. Barata, and T. Mendonça, "On the role of texture and color in the classification of dermoscopy images," in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012, pp. 4402-4405.

F. Riaz, A. Hassan, M. Y. Javed, and M. T. Coimbra, "Detecting melanoma in dermoscopy images using scale adaptive local binary patterns," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, pp. 6758-6761.

C. Barata, J. S. Marques, and J. Rozeira, "Evaluation of color based keypoints and features for the classification of melanomas using the bag-of-features model," in International Symposium on Visual Computing, 2013, pp. 40-49.

C. Barata, M. E. Celebi, and J. S. Marques, "Improving dermoscopy image classification using color constancy," IEEE journal of biomedical and health informatics, vol. 19, pp. 1146-1152, 2015.




How to Cite

OZKAN, I. A., & KOKLU, M. (2017). Skin Lesion Classification using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 285–289. Retrieved from



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