Automatic dog breed classification using deep learning

Research Article
Open access

Automatic dog breed classification using deep learning

T Kumaravel 1* , P Natesan 2 , V E Sathishkumar 3 , Sathiya Shri N 4 , Swathy G 5 , Uvetha V 6
  • 1 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India    
  • 2 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India    
  • 3 Department of Software Engineering, Jeonbuk National University, Jeonji-si, Jeollabuk-do, Jeonju-si, Republic of Korea    
  • 4 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India    
  • 5 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India    
  • 6 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India    
  • *corresponding author sathish@jbnu.ac.kr
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230968
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Dogs are a common type of animal that can present various problems, such as issues with population control, controlling rabies outbreaks, administering vaccinations, and legal ownership, due to their large numbers. Understanding the breed of the dog can help the owner identify potential health issues and determine their lifespan. There are over 120 different breeds of dogs, each with unique characteristics and health concerns. To properly care for and train a dog, it is important to know their breed. This study discusses methods of classifying dog breeds and presents a method using a CNN (Convolutional Neural Network) to accurately identify different breeds by analyzing dog images. This approach, using DenseNet201, achieved an accuracy of 87.34% on the Dog Breed Images dataset and is more effective than other methods found in literature.

Keywords:

Dog breed, Convolutional Neural Network (CNN), Dog Breed Classification Dataset, DenseNet, Xception, Inception

Kumaravel,T.;Natesan,P.;Sathishkumar,V.E.;N,S.S.;G,S.;V,U. (2023). Automatic dog breed classification using deep learning. Applied and Computational Engineering,6,962-968.
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References

[1]. Sandra Varghese and Remya S. “Dog breed classification using CNN”, pp. 1097-1105. 2021.

[2]. Sanabel Abu Jwade and Ajmal Mian, Andrew Guzzomi, “On farm automatic sheep breed classification using deep learning,” Animals, vol. 2, no. 2, pp. 301–315, 2019.

[3]. Tracey Clarke, Daniel Mills and Jonathan Cooper, “Exploring the utility of traditional breed group classification as an explanation of problem solving behaviour of domestic dogs, vol. 7, no. 5, pp. 470–482, 2019.

[4]. R. Kumar M. Sharma K. Dhawale and G. Singal "Identification of Dog Breeds Using Deep Learning" Proc. 2019 IEEE 9th Int. Conf. Adv. Comput. IACC 2019 pp. 193-198 2019.

[5]. Malliga Subramanian, Kogilavani Shanmugavadivel, Obuli Sai Naren, K Premkumar, K Rankish. "Classification of Retinal OCT Images Using Deep Learning", 2022 International Conference on Computer Communication and Informatics (ICCCI), 2022

[6]. KAGGLE – Dog Breed Clasification Images https://www.kaggle.com/datasets/uvetha/dog-breeds-rrr

[7]. Rajalaxmi, R. R., Saradha, M., Fathima, S. K., Sathish Kumar, V. E., Sandeep kumar, M., & Prabhu, J. (2022). An Improved MangoNet Architecture Using Harris Hawks Optimization for Fruit Classification with Uncertainty Estimation. Journal of Uncertain Systems.

[8]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[9]. Subramanian, M., Rajasekar, V., VE, S., Shanmugavadivel, K., & Nandhini, P. S. (2022). Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification. Electronics, 11(24), 4117.

[10]. Shanmugavadivel, K., Sathishkumar, V. E., Kumar, M. S., Maheshwari, V., Prabhu, J., & Allayear, S. M. (2022). Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images. Computational & Mathematical Methods in Medicine.

Cite this article

Kumaravel,T.;Natesan,P.;Sathishkumar,V.E.;N,S.S.;G,S.;V,U. (2023). Automatic dog breed classification using deep learning. Applied and Computational Engineering,6,962-968.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Sandra Varghese and Remya S. “Dog breed classification using CNN”, pp. 1097-1105. 2021.

[2]. Sanabel Abu Jwade and Ajmal Mian, Andrew Guzzomi, “On farm automatic sheep breed classification using deep learning,” Animals, vol. 2, no. 2, pp. 301–315, 2019.

[3]. Tracey Clarke, Daniel Mills and Jonathan Cooper, “Exploring the utility of traditional breed group classification as an explanation of problem solving behaviour of domestic dogs, vol. 7, no. 5, pp. 470–482, 2019.

[4]. R. Kumar M. Sharma K. Dhawale and G. Singal "Identification of Dog Breeds Using Deep Learning" Proc. 2019 IEEE 9th Int. Conf. Adv. Comput. IACC 2019 pp. 193-198 2019.

[5]. Malliga Subramanian, Kogilavani Shanmugavadivel, Obuli Sai Naren, K Premkumar, K Rankish. "Classification of Retinal OCT Images Using Deep Learning", 2022 International Conference on Computer Communication and Informatics (ICCCI), 2022

[6]. KAGGLE – Dog Breed Clasification Images https://www.kaggle.com/datasets/uvetha/dog-breeds-rrr

[7]. Rajalaxmi, R. R., Saradha, M., Fathima, S. K., Sathish Kumar, V. E., Sandeep kumar, M., & Prabhu, J. (2022). An Improved MangoNet Architecture Using Harris Hawks Optimization for Fruit Classification with Uncertainty Estimation. Journal of Uncertain Systems.

[8]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[9]. Subramanian, M., Rajasekar, V., VE, S., Shanmugavadivel, K., & Nandhini, P. S. (2022). Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification. Electronics, 11(24), 4117.

[10]. Shanmugavadivel, K., Sathishkumar, V. E., Kumar, M. S., Maheshwari, V., Prabhu, J., & Allayear, S. M. (2022). Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images. Computational & Mathematical Methods in Medicine.

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