Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Classification of Skin Cancers using Radial basis Function Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Sasirooba Thirumavalavan, Sasikala Jayaraman

Sasirooba Thirumavalavan and Sasikala Jayaraman. Classification of Skin Cancers using Radial basis Function Network. International Journal of Computer Applications 151(4):19-22, October 2016. BibTeX

	author = {Sasirooba Thirumavalavan and Sasikala Jayaraman},
	title = {Classification of Skin Cancers using Radial basis Function Network},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {151},
	number = {4},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {19-22},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016911733},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper suggests a model for classifying skin lesions into benign and malignant melanoma using radial basis function network (RBFN). The model initially converts the color image into gray image and then applies Median filter for removing thin hairs and other noises. It then segments the cancerous region through segmentation and then extracts features that represent the characteristics of the skin lesion. The RBFN then processes the computed features and classifies the skin lesion either as a benign or a malignant. The paper discusses with intermediate results on sample skin images and exhibits the elegant performance of the suggested model.


  1. Hoshyar AN, Al-Jumaily A, Sulaiman R. Review on automatic early skin cancer detection. InComputer Science and Service System (CSSS), 2011 International Conference, IEEE, 2011; 4036-4039.
  2. Loescher LJ, Janda M, Soyer HP, Shea K, Curiel-Lewandrowski C. Advances in skin cancer early detection and diagnosis. InSeminars in oncology nursing, WB Saunders, 2013; 29(3):170-181.
  3. Almaraz-Damian JA, Ponomaryov V, Rendon-Gonzalez E. Melanoma CADe based on ABCD Rule and Haralick Texture Features. In2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW), IEEE, 2016; 1-4.
  4. Abbas Q, EmreCelebi M, Garcia IF, Ahmad W. Melanoma recognition framework based on expert definition of ABCD for dermoscopic images. Skin Research and Technology, 2013;19(1):93-102.
  5. Mete M, Sirakov NM. Optimal set of features for accurate skin cancer diagnosis. In2014 IEEE International Conference on Image Processing (ICIP), IEEE, 2014; 2256-2260.
  6. Kasmi R, Mokrani K. Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Processing, 2016;10(6):448-55.
  7. Cheng YI, Swamisai R, Umbaugh SE, Moss RH, Stoecker WV, Teegala S, Srinivasan SK. Skin lesion classification using relative color features. Skin Research and Technology, 2008;14(1):53-64.
  8. Ramteke RJ, KhachaneMonali Y. Automatic medical image classification and abnormality detection using K-Nearest Neighbour. International Journal of Advanced Computer Research, 2012;2(4):190-6.
  9. Lau HT, Al-Jumaily A. Automatically Early Detection of Skin Cancer: Study Based on NueralNetwok Classification. InSoft Computing and Pattern Recognition, SOCPAR'09. International Conference, IEEE, 2009; 375-380.
  10. Schmid-Saugeona P, Guillodb J, Thirana JP. Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics, 2003;27(1):65-78.
  11. Ercal F, Chawla A, Stoecker WV, Lee HC, Moss RH. Neural network diagnosis of malignant melanoma from color images. IEEE Transactions on biomedical engineering, 1994;41(9):837-45.
  12. Maglogiannis I, Doukas CN. Overview of advanced computer vision systems for skin lesions characterization. IEEE transactions on information technology in biomedicine, 2009;13(5):721-33.
  13. Ruiz D, Berenguer V, Soriano A, SáNchez B. A decision support system for the diagnosis of melanoma: A comparative approach. Expert Systems with Applications, 2011;38(12):15217-23.
  14. Barata C, Ruela M, Francisco M, Mendonça T, Marques JS. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 2014;8(3):965-79.
  15. Shrestha B, Bishop J, Kam K, Chen X, Moss RH, Stoecker WV, Umbaugh S, Stanley RJ, Celebi ME, Marghoob AA, Argenziano G. Detection of atypical texture features in early malignant melanoma. Skin Research and Technology, 2010;16(1):60-5.
  16. Savitha R, Suresh S, Sundararajan N, Kim HJ. A fully complex-valued radial basis function classifier for real-valued classification problems. Neurocomputing, 2012;78(1):104-10.
  17. Przystalski K, Nowak L, Ogorzalek M, Surówka G. Semantic analysis of skin lesions using radial basis function neural networks. In3rd International Conference on Human System Interaction, IEEE, 2010; 128-132.
  18. Fernández-Navarro F, Hervás-Martínez C, Gutiérrez PA. Generalised Gaussian radial basis function neural networks. Soft Computing, 2013;17(3):519-33.
  19. Dermatology Information System,, 2016, Accessed: 15 Jun 2016.
  20. DermQuest,, 2016, Accessed: 15 Jun 2016.


denoising, segmentation, radial basis function network.