CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Chronic Skin Diseases using Artificial Neural Network

by Sudhakar Singh, Shabana Urooj
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 31
Year of Publication: 2018
Authors: Sudhakar Singh, Shabana Urooj
10.5120/ijca2018915290

Sudhakar Singh, Shabana Urooj . Analysis of Chronic Skin Diseases using Artificial Neural Network. International Journal of Computer Applications. 179, 31 ( Apr 2018), 7-13. DOI=10.5120/ijca2018915290

@article{ 10.5120/ijca2018915290,
author = { Sudhakar Singh, Shabana Urooj },
title = { Analysis of Chronic Skin Diseases using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 31 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 7-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number31/29192-2018915290/ },
doi = { 10.5120/ijca2018915290 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:57:05.483064+05:30
%A Sudhakar Singh
%A Shabana Urooj
%T Analysis of Chronic Skin Diseases using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 31
%P 7-13
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel method of skin diseases classification. The complete work is divided into four parts. First is preprocess the image then segment the image by using modified sobel edge detection technique, and extract the features of the segmented image, extracted features are sub divided in to sub space features and calssified the features by artificial Neural Network(ANN). The performance of the different training algorithm has been investigated. Mean Square Error (MSE) is evaluated. Bayesian regularization backpropagation algorithm gives minimum MSE is 4.8561e-13 and gradient is 1.6337e-08 at 190 epochs. Levenberg-Marquardt backpropagation algorithm provides MSE 1.0559e-10 and gradient is 9.9001e-08 at 105 epochs. Resilient backpropagation algorithm 3.5354e-07 and gradient is 8.5468e-06 at 347 epochs. Scaled conjugate gradient backpropagation algorithm give MSE 0.02269 and gradient is 8.6124e-07 at 115 epochs.

References
  1. Argenziano, Giuseppe, and H. Peter Soyer. "Dermoscopy of pigmented skin lesions–a valuable tool for early." The lancet oncology 2, no. 7 (2001): 443-449.
  2. Vestergaard, M. E., P. H. P. M. Macaskill, P. E. Holt, and S. W. Menzies. "Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting." British Journal of Dermatology 159, no. 3 (2008): 669-676.
  3. Ascierto, P. A., G. Palmieri, E. Celentano, R. Parasole, C. Caraco, A. Daponte, M. G. Chiofalo et al. "Sensitivity and specificity of epiluminescence microscopy: evaluation on a sample of 2731 excised cutaneous pigmented lesions." British Journal of Dermatology 142, no. 5 (2000): 893-898.
  4. Stanley, R. Joe, Randy Hays Moss, William Van Stoecker, and Chetna Aggarwal. "A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images." Computerized Medical Imaging and Graphics 27, no. 5 (2003): 387-396.
  5. H. Peter Soyer, Harold S. Rabinovitz, and Armand B. Cognetta. "Fuzzy logic techniques for blotch feature evaluation in dermoscopy images." Computerized Medical Imaging and Graphics 33, no. 1 (2009): 50-57.
  6. Xiao, Kong, Lin Danghui, and Shen Lansun. "Segmentation of skin color regions based on fuzzy cluster." In Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, pp. 125-128. IEEE, 2004.
  7. Schmid, Philippe. "Segmentation of digitized dermatoscopic images by two-dimensional color clustering." IEEE Transactions on Medical Imaging 18, no. 2 (1999): 164-171.
  8. Pan, Meisen, Jingtian Tang, and Qi Xiong. "Medical image registration using fuzzy theory." Computer methods in biomechanics and biomedical engineering 15, no. 7 (2012): 721-734.
  9. Bhatt, Rajen B., Gaurav Sharma, Abhinav Dhall, and Santanu Chaudhury. "Efficient skin region segmentation using low complexity fuzzy decision tree model." In India Conference (INDICON), 2009 Annual IEEE, pp. 1-4. IEEE, 2009.
  10. Ma, Zhen, João Manuel RS Tavares, Renato Natal Jorge, and T. Mascarenhas. "A review of algorithms for medical image segmentation and their applications to the female pelvic cavity." Computer Methods in Biomechanics and Biomedical Engineering 13, no. 2 (2010): 235-246.
  11. Chuang, Shao-Hui, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, and Frederic D. McKenzie. "BCC skin cancer diagnosis based on texture analysis techniques." In SPIE Medical Imaging, pp. 79633O-79633O. International Society for Optics and Photonics, 2011.
  12. Deng, Yining, and B. S. Manjunath. "Unsupervised segmentation of color-texture regions in images and video." IEEE transactions on pattern analysis and machine intelligence 23, no. 8 (2001): 800-810.
  13. Geman, Stuart, and Donald Geman. "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images." IEEE Transactions on pattern analysis and machine intelligence 6 (1984): 721-741.
  14. Vincent, O. R., and Olusegun Folorunso. "A descriptive algorithm for sobel image edge detection." In Proceedings of Informing Science & IT Education Conference (InSITE), vol. 40, pp. 97-107. 2009.
  15. Urooj, Shabana, and Sudhakar Singh. "A novel computer assisted approach for diagnosis of skin disease." In Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, pp. 1585-1590. IEEE, 2015.
  16. Flusser, Jan, Barbara Zitová, and Toms Suk. "Invariant-based registration of rotated and blurred images." In Geoscience and Remote Sensing Symposium, 1999. IGARSS'99 Proceedings. IEEE 1999 International, vol. 2, pp. 1262-1264. IEEE, 1999.
  17. Chen, Qing, Emil Petriu, and Xiaoli Yang. "A comparative study of Fourier descriptors and Hu's seven moment invariants for image recognition." In Electrical and Computer Engineering, 2004. Canadian Conference on, vol. 1, pp. 103-106. IEEE, 2004.
  18. Ma, Zhen, João Manuel RS Tavares, Renato Natal Jorge, and T. Mascarenhas. "A review of algorithms for medical image segmentation and their applications to the female pelvic cavity." Computer Methods in Biomechanics and Biomedical Engineering 13, no. 2 (2010): 235-246.
  19. Van Leemput, Koen, Frederik Maes, Dirk Vandermeulen, and Paul Suetens. "Automated model-based tissue classification of MR images of the brain." IEEE transactions on medical imaging 18, no. 10 (1999): 897-908.
  20. Manjunath, Bangalore S., and Wei-Ying Ma. "Texture features for browsing and retrieval of image data." IEEE Transactions on pattern analysis and machine intelligence 18, no. 8 (1996): 837-842.
  21. Yang, Jianchao, Kai Yu, Yihong Gong, and Thomas Huang. "Linear spatial pyramid matching using sparse coding for image classification." In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 1794-1801. IEEE, 2009.
  22. Benenson, Rodrigo, Markus Mathias, Radu Timofte, and Luc Van Gool. "Pedestrian detection at 100 frames per second." In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2903-2910. IEEE, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation Feature extraction Feature selection ANN Classification

Powered by PhDFocusTM