There are multiple techniques used in image classification such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy measures, and Fuzzy Support Vector Machines (FSVM). Classification of muzzle depending on one of this artificial technique has become widely known for guaranteeing the safety of cattle products and assisting in veterinary disease supervision and control. The aim of this paper is to focus on using neural network technique for image classification. First, the area of interest in the captured image of muzzle is detected then pre- processing operations such as histogram equalization and morphological filtering have been used for increasing the contrast and removing noise of the image. Then, using box-counting algorithm to extract the texture feature of each muzzle. This feature is used for learning and testing stage of the neural network for muzzle classification. The experimental result shows that after 15 input cases for each image in neural training step, the testing result is true and gives us the correct muzzle detection. Therefore, neural networks can be applied in classification of bovines for breeding and marketing systems registration.

[1] Anitha S. and Radha V., “Comparison of Image Preprocessing Techniques for Textile Texture Images,” International Journal of Engineering Science and Technology, vol. 2, no. 12, pp. 7619- 7625, 2010.

[2] Balan A., Traina A., Traina C., and Azevedo- Marques P., “Fractal Analysis of Image Textures for Indexing and Retrival by Content,” in Proceeding of 18th IEEE Symposium on Computer-Based Medical Systems, Bangkok, pp. 581-586, 2005.

[3] Bandyopadhyay S., “Pre-Processing of Mammogram Images,” International Journal of Engineering Science and Technology, vol. 2, no. 11, pp. 6753-6758, 2010.

[4] Baranov A., Graml R., Pirchner F., and Schmid D., “Breed Differences and Intra-Breed Genetic Variability of Dermatoglyphic Pattern of Cattle,” Journal of Animal Breeding and Genetics, vol. 110, no. 1-6, pp. 385-392, 1993.

[5] Basu J., Bhattacharyya D., and Kim T., “Use of Artificial Neural Network in Pattern Recognition,” International Journal of Software Engineering and its Applications, vol. 4, no. 2, pp. 23-34, 2010.

[6] Devijver P. and Kittler J., Pattern Recognition: A Statistical Approach, Prentice Hall, 1982.

[7] Droogenbroeck M. and Buckley M., “Morphological Erosions and Openings: Fast Algorithms based on Anchors,” Journal of Mathematical Imaging and Vision, vol. 22, no. 2, pp. 121-142, 2005.

[8] Dubey R., Hanmandlu M., and Gupta S., “A Comparison of Two Methods for the Segmentation of Masses in the Digital Mammograms,” Computerized Medical Imaging and Graphics, vol. 34, no. 3, pp. 185-191, 2010.

[9] Egmont-Petersen M., Ridder D., and Handels H., “Image Processing with Neural Networks-a Review,” The Journal of the Pattern Recognition Society, vol. 35, no. 10, pp. 2279-2301, 2002.

[10] El-Bakry H., El-Hennawy I., and El-Hadad H., “Bovines Muzzle Identification using Box- Counting,” International Journal of Computer Science and Information Security, vol. 12, no. 5, pp. 29-34, 2014.

[11] El-Henawy I., El-Bakry H., and El-Hadad H., “Cattle Identification using Segmentation-based Fractal Texture Analysis and Artificial Neural Networks,” International Journal of Electronics and Information Engineering, vol. 4, no. 2, pp. 82-93, 2016.

[12] El-Henawy I., El-Bakry H., El-Hadad H., and Mastorakis N., “Muzzle Feature Extraction Based on Gray Level Co-Occurrence Matrix,” International Journal of Veterinary Medicine, vol. 1, pp.16-24, 2016.

[13] El-Henawy I., El-Bakry H., El-Hadad H., and Mastorakis N., “Bovines’ Texture Feature Extraction Based on Discrete Wavelet Transform,” International Journal of Veterinary Medicine, vol. 1, pp.5-15, 2016.

[14] Fukunaga K., Introduction to Statistical Pattern Recognition, Academic Press, 1990.

[15] Gonzalez R., Woods R., and Eddins S., Digital Image Processing using MATLAB, Pearson 472 The International Arab Journal of Information Technology, Vol. 14, No. 4, July 2017 Education, 2004.

[16] Gonzalez R. and Woods R., Digital Image Processing, Prentice-Hall, 2008.

[17] Graupe D., Principles of Artificial Neural Networks, World Scientific, 2007.

[18] Günther F. and Fritsch S., “Neuralnet: Training of Neural Networks,” The R Journal, vol. 2/1, no. 1, pp. 30-38, 2010.

[19] Jain A., Mao J., and Mohiuddin K., “Artificial Neural Networks: A Tutorial,” Computer, vol. 29, no. 3, pp. 31- 44, 1996.

[20] Kohonen T., Self-Organizing Maps, Springer- Verlage, 1995.

[21] Lal S. and Chandra M., “Efficient Algorithm for Contrast Enhancement of Natural Images,” The International Arab Journal of Information Technology, vol. 11, no. 1, pp. 95-102, 2014.

[22] MacLennan J., Tang Z., and Crivat B., Data Mining with Microsoft SQL Server, Wiley, 2008.

[23] Mehena J., “Medical Images Edge Detection Based on Mathematical Morphology,” International Journal of Computer and Communication Technology, vol. 2, no. 6, pp. 49- 53, 2011.

[24] Ponraj D., Jenifer M., Poongodi P., and Manoharan J., “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer,” Journal of Emerging Trends in Computing and Information Sciences, vol. 2, no. 12, pp. 656-664, 2011.

[25] Rumelhart D. and McClelland J., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, 1986.

[26] Thai L., Hai T., and Thuy N., “Image Classification using Support Vector Machine and Artificial Neural Network,” Information Technology and Computer Science, vol. 4, no. 5, pp. 32-38, 2012.

[27] Thangavel K. and Roselin R., “Mammogram Mining with Genetic Optimization of Ant-Miner Parameters,” International Journal of Recent Trends in Engineering, vol. 2, no. 3, pp. 67-69, 2009.

[28] Tian D., “A Review on Image Feature Extraction and Representation Techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.

[29] Vatsa M., Chauhan D., and Singh S., “Image Contrast using Histogram Equalization,” in Proceeding of I-Society, Punjab, pp. 1-7, 2012.

[30] Wang Y., Chen Q., and Zhang B., “Image Enhancement based on Equal Area Dualistic Sub-Image Histogram Equalization Method,” IEEE Transaction on Consumer Electronics, vol. 45, no. 1, pp. 68-75, 1999.

[31] Yeganeh H., Ziaei A., and Rezaie A., “A Novel Approach for Contrast Enhancement Based on Histogram Equalization,” in Proceeding of the International Conference on Computer and Communication Engineering, Coimbatore, pp. 256-260, 2008. Ibrahim El-henawy received the M.S. and Ph.D. degrees in computer science from State University of New York, USA in 1980 and 1983, respectively. Currently, he is a professor in computer science and mathematics department, Zagazig University. His current research interests are mathematics, operations research, statistics, networks, optimization, Intelligent Computing, Computer Theory, digital image processing, and pattern recognition. Hazem El-Bakry (Mansoura, EGYPT 20-9-1970) received B.Sc. degree in Electronics Engineering, and M.Sc. in Electrical Communication Engineering from the Faculty of Engineering, Mansoura University - Egypt, in 1992 and 1995 respectively. Dr. El-Bakry received Ph. D degree from University of Aizu-Japan in 2007. Currently, he is associate professor at the Faculty of Computer Science and Information Systems - Mansoura University-Egypt. His research interests include neural networks, pattern recognition, image processing, biometrics, cooperative intelligent systems and electronic circuits. In these areas, he has published many papers in major international journals and refereed international conferences. According to academic measurements, now the total number of citations for his publications is 2899. The H-index of his publications is 28. Dr. El-Bakry has the United States Patent No. 20060098887, 2006. Furthermore, he is associate editor and referee for some major journals. Moreover, he has been awarded the Japanese Computer and Communication prize in April 2006 and the best paper prize in two conferences cited by ACM. Dr. El-Bakry has been selected in who Asia 2006 and BIC 100 educators in Africa 2008. Hagar El-Hadad graduated from Faculty of Computers and Information, Minia University, Minia, Egypt in 2008. Hagar received her master degree in 2011 in Information Systems from the Faculty of Computers and Information, Mansoura University, Mansoura, Egypt. Hagar is teaching assistant in faculty of computer and information systems, Beni-Suef University Beni-Suef, Egypt. Hagar main research interests are in the areas of data mining such as (text-numbers-Images).