- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Gout Images Detection and Recognition by Neural Network | ||
| Journal of University of Anbar for Pure Science | ||
| Article 23, Volume 5, Issue 2, 2011, Pages 103-108 PDF (452.2 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.37652/juaps.2011.44190 | ||
| Author | ||
| Hind Rostom Mohammed* | ||
| College of mathematical and computer sciences- Kufa University | ||
| Abstract | ||
| Gout skin detection and tracking has been the topics of an extensive research for the several past decades. Many heuristic and pattern recognition based strategies have been proposed for achieving robust and accurate solution.This paper demonstrates how a Gout skin detection recognition system can be designed with artificial neural network. Note that the training process did not consist of a single call to a training function. Instead, the network was trained several times on various input ideal and noisy images, the images which contents Gout skin . The objective of this study was to develop a back propagation artificial neural network (ANN) model that could distinguish gout image by several parameters for testing are Energy , Entropy , Average andVariance. Although only the color indices associated with image pixels were used as inputs, it was assumed that the ANN model could develop the ability to use other information, such as shapes, implicit in these data. The 756x504 pixel images were taken in the field and were then cropped to 100x100-pixel images in testing phase. A total of ٨٠ images of gout image and other images were used for training purposes. For ANNs, the success rate for classifying gout image was as high as 100% . | ||
| Keywords | ||
| Back propagation artificial neural network; Image Processing; Color space; Model selection; Skin Detection | ||
| References | ||
|
[1] Burger, W., Burge, M.: Digital Image Processing, an AlgorithmicIntroduction Using Java. Springer (2008)
[2] Rein-Lien Hsu, “Face Detection and Modeling for Recognition,” PhDthesis, Department of Computer Science & Engineering, Michigan StateUniversity, USA, 2002.
[3] Tom Mitchell , ‘’ neural net & Face images” , Home work 3, CMU,Carnegie Mellon University, Pittsburgh, USA, October 1997.
[4] Wang, D., F.E. Dowell and R.E. Lacey. 1999. Single wheat kernelcolor classification using neural networks. Transactions of the ASAE 42:233-240.
[5] DECT For Gout Diagnosis by GoutPal.com is licensed under a Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales License. Permissions beyond the scope of this license may be available at http://www.goutpal.com/gouty/ gout-people.
[6]http://www.ihe-online.com/e-news/details/ archive/2009/june/article/new-diagnostic-method-for-gout-dual-energy-computed-tomography-instead-of-joint-aspiration/index.html, New diagnostic method for gout: Dual Energy Computed tomography instead of joint aspiration.
[7] Dmitry Davidov, Ari Rappoport,” Enhancement of Lexical Concepts Using Cross-lingualWeb Mining”, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 852–861, Singapore, 6-7 August 2009. c 2009 ACL and AFNLP.
[8] Enrique Corona, Sunanda Mitra,” Digital Stereo Image Analyzer for Generating Automated 3-D Measures of OpticDisc Deformation in Glaucoma",IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 21, NO. 10, OCTOBER 2002.
[9] Leila Fallah Araghi, Hamed Shahhosseini, Farbod Setoudeh,” IRIS Recognition Using Neural Network”,Precedings of the International Multi Conference of Engineers and computer Scientists 2010,Vol(I), march,Hong Kong(2010).
| ||
|
Statistics Article View: 454 PDF Download: 413 |
||