Skin Cancer Detection Using Support Vector Machine Learning Classification based on Particle Swarm Optimization Capabilities

  • Ding-Yu Fei Virginia Commonwealth University
  • Osamah Almasiri Virginia Commonwealth University
  • Azhar Rafig Virginia Commonwealth University
Keywords: Skin cancer detection, Melanoma, Benign, Particle swarm optimization (PSO), Image feature extraction, Support vector machine (SVM)


Skin cancer continues to be a common malignancy that has steadily increased each year. The need for early detection of such skin lesions is critical to preventing further medical complications. The main method for detection of skin cancer is by microscopic examination of skin lesions. Great efforts have been placed to use computer aided technologies for the analysis of skin lesions. In this study, we present a method for an algorithm design using Support Vector Machine (SVM) learning classification based on Particle swarm optimization (PSO) principles in order to improve the accuracy of skin lesion image analysis and classification for further diagnosis. Hospital Pedro Hispano (PH²) dataset with 200 images is used for this study. The method presented here incorporates 46 texture features in order to complete comprehensive image analytics and classification. The proposed method demonstrates an opportunity to explore best possible criteria in image analytics for clinical decision support.


(1) American Cancer Society. Cancer facts & figures 2016. Atlanta, American Cancer Society, 2016.

(2) American Cancer Society. Cancer facts and figures 2018.

(3) Shivangi Jain, Vandana jagtap, and Nitin Pise. Computer aided melanoma skin cancer detection using image processing, Elsevier B.V.,, Procedia Computer Science 48, p. 735 – 740, 2015.

(4) Centers for Disease Control and Prevention, Skin cancer statistics,, Page Last Update June 7, 2017.

(5) G.Argenziano, H. P. Soyer, V. De Giorgio et al., Interactive atlas of dermoscopy, Edra Medical Publishing and New Media, Milano, Italy, 2000.

(6) Leslie K. Dennis, Marta J. Vanbeek, Laura E. Beane Freeman, Brian J. Smith, Deborah V. Dawson and Julie A. Coughlin, Sunburns and risk of cutaneous melanoma: Does age matter? A comprehensive meta-analysis, Annals of Epidemiology, Vol. 18 ( 8 ), pp. 614–627, August 2008.

(7) Argenziano, G., Soyer, H.P., Chimenti, S., Talamini, R., Corona, R., Sera, F., Binder, M., Cerroni, L., De Rosa, G., Ferrara, G., Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet, Journal of the American Academy of Dermatology, Elsevier, Vol. 48 ( 5 ), pp. 679–693, May 2003.

(8) Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang, Artificial intelligence in healthcare: past, present and future , Stroke and Vascular Neurology 2017; 0:e000101. doi:10.1136/svn-2017-000101, BMJ Publishing Group Ltd, 2017.

(9) van der Waal, I. Skin cancer diagnosed using artificial intelligence on clinical images. Oral Dis. Mar. 2017.

(10) Pooja Kamavisdar, Sonam Saluja, Sonu Agrawal, A survey on image classification approaches and techniques, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2 ( 1 ), January 2013.

(11) A. Bono, C. Bartoli, M. Baldi Bono A1, Bartoli C, Baldi M, Moglia D, Tomatis S, Tragni G, Cascinelli N, Santinami M. Micro-melanoma detection: A clinical study on 22 cases of melanoma with a diameter equal to or less than 3

mm, Tumori, Vol. 90 (1), pp 128–131, 2004.

(12) Al. Abadi, N. K., Dahir, N. S., Alkareem, Z. A. Skin texture recognition using neural network, Proc. of the International Arab Conference on Information Technology, Tunisia, pp. 1-4, December, 2008.

(13) Jonathan Blackledge, Dymitiy A. Dubovitskiy. Texture classification using fractal geometry for the diagnosis of skin cancers ", Proc. of EG UK Theory and Practice of Computer Graphics, UK, pp. 1-8, 2009.

(14) R. Eberhart, J. Kennedy, A new optimizer using Particle Swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (Nagoya, Japan), IEEE service center, Piscataway, NJ: pp 39-43, 1995.

(15) Riccardo Poli, James Kennedy, Tim Blackwell, Particle swarm optimization: An overview, Swarm Intell, DOI 10.1007/s11721-007-0002-0, 2007.

(16) Bruno Seixas Gomes de Almeida, Victor Coppo Leite, Particle swarm optimization: A powerful technique for solving engineering problems, Open access chapter, DOI: 10.5772/intechopen 89633, 2019.

(17) Rafael C. Gonzalez, Richard E. Woods. Digital image processing, Pearson Prentice Hall, Third Edition, 2008.

(18) G. Capdenhourat, A. Corez, A. Bazzano, R. Allonso, P. Musé, Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions, Pattern Recognition Letters, Vol. 32, pp. 2187-2196, 2011.

(19) A. Safi, M. Baust, O. Pauly, V. Castaneda, T. Lasser, D. Mateus, N. Navab, R.Hein, M. Ziai, Computer Aided Diagnosis of Pigmented Skin Dermoscopic Images,, Lecture Notes on Computer Science , Vol. 7075, pp. 105–115, 2012.

(20) Uzmabano Ansari, Tanujasarode , Skin cancer detection using SVM , Proceedings of WRFER International Conference, Pune, India, April 2017.

(21) Genton, Marc G., Classes of kernels for machine learning: A statistics perspective, Journal of Machine Learning Research, Vol. 2, pp. 299-312, 2001.

(22) PH² dataset: Accessed on 04/12/2018.

(23) Azadeh Noori Hoshyar, Adel Al-Jumailya, Afsaneh Noori Hoshyar, The beneficial techniques in preprocessing step of skin cancer detection system comparing , International Conference on Robot PRIDE 2013-2014 – Medical

and Rehabilitation Robotics and Instrumentation, Elsevier Procedia Computer Science 42, pp. 25 – 31, 2014.

(24) Alina Sultana, Mihai Ciuc, Tiberiu Radulescu,Liu Wanyu, Diana Petrache, Preliminary work on dermatoscopic lesion segmantation , 20th European Signal Processing Conference (EUSIPCO 2012), Bucharest, Romania, August

-31, 2012.

(25) Kumari, Amita, Mehra, Dr. Rajesh. Hybridized classification of brain MRI using PSO & SVM. International Journal of Engineering and Advanced Technology. Vol. 3. pp. 319-323, 2014.

(26) Gillinder Bedi, Facundo Carrillo, Guillermo A Cecchi et al., Automated analysis of free speech predicts psychosis onset in high risk youths. NPJ Schizophr; 1:15030, 2015.

(27) Wall DP1, Kosmicki J, Deluca TF, Harstad E, Fusaro VA, Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry, Apr 10; 2:e100, 2012.

(28) Catarina Barata, Margarida Ruela, Mariana Francisco, Teresa Mendonça, Jorge S. Marques, Two systems for the detection of melanomas in dermoscopy images using texture and color features, IEEE Systems Journal, VOL. 8 ( 3 ), September 2014.

(29) Joel Than Chia Ming, Norliza Mohd Noor, Omar Mohd Rijal, Rosminah M. Kassim, Ashari Yunus, Lung disease classification using different deep learning architectures and principal component analysis, 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS). July 2018.

How to Cite
Fei, D.-Y., Almasiri, O., & Rafig, A. (2020). Skin Cancer Detection Using Support Vector Machine Learning Classification based on Particle Swarm Optimization Capabilities. Transactions on Machine Learning and Artificial Intelligence, 8(4), 01-13.