Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

Revati Kadu, U. A. Belorkar


One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.

Full Text:



D.N.V.S.L.S. Indira # , JYOTSNA SUPRIYA P , Detection & Analysis of Skin Cancer using Wavelet Techniques, International Journal of Computer Science and Information Technologies, Vol. 2 (5) , 2011, 1927-1932

D.S. Zingade, Manali Joshi,,Skin Disease Detection using Artificial Neural Network ,International Journal of Advance Engineering and Research Development Special Issue on Recent Trends in Data Engineering Volume 4, Special Issue 5, Dec.-2017 @IJAERD-2017,

D.S. Zingade1, Manali Joshi2, Viraj Sapre3, Rohan Giri4 Maglogiannis and C. Doukas. Overview of advanced computer vision systems for skin lesions characterization.IEEE Trans. on Information Technology in Biomedicine,

13(5):721–733, 2009American Academy of Dermatology, 56(3):417–421, 2007R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, “High-speed digital-to-RF converter,” U.S. Patent 5 668 842, Sept. 16, 1997.

A. Perrinaud, O. Gaide, L. French, J. Saurat, A. Marghoob,and R. Braun. Can automated dermoscopy image analysisinstruments provide added benefit for the dermatologist? Astudy comparing the results of three systems. British Journal of Dermatology, 157:926–933, 2007

M. Elbaum, A. Kopf, H. Rabinovitz, R. Langley, andH. Kamino. Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: Afeasibility study. Journal of the American Academy of Dermatology.

M. Shamsul Ari n, M. Golam Kibria, Adnan Firoze, M. Ashraful Amin, Hong Yan,"DermatologicalDiseaseDiagnosis Using Color-skin Images", Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2015

L. G. Kabari and F. S. Bakpo, Member, IEEE, "Diagnosing Skin Diseases Using an Artificial Neural Network", 2016IEEE:

Wasan Kadhim Saa'd, Method For Detection And Diagnosis Of The Area Of Skin Disease Based On Color By Wavelet Transform And Artificial Neural Network, Al-Qadisiya Journal For Engineering Sciences

Girish Patil , Karan Belsare, An Approach to Natural Image Classification based on Wavelet Transform and KNN, International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-4, Issue-6) June 2015



  • There are currently no refbacks.

© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.