Automatic classification of Breeds of Dog using Convolutional Neural Network

Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed simila...

Full description

Bibliographic Details
Main Authors: P. O. Adejumobi, I. O. Adejumobi, O. A. Adebisi, S. O. Ayanlade, I. I. Adeaga
Format: Article
Language:English
Published: Faculty of Engineering and Technology 2023-09-01
Series:Nigerian Journal of Technological Development
Subjects:
Online Access:https://journal.njtd.com.ng/index.php/njtd/article/view/1485
_version_ 1797678050710650880
author P. O. Adejumobi
I. O. Adejumobi
O. A. Adebisi
S. O. Ayanlade
I. I. Adeaga
author_facet P. O. Adejumobi
I. O. Adejumobi
O. A. Adebisi
S. O. Ayanlade
I. I. Adeaga
author_sort P. O. Adejumobi
collection DOAJ
description Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed similarities and intra breed differences thereby creating a difficulty in their classification. The American Kennel Club (AKC) classified breeds of dog into groups based on characteristic, purpose, behaviuor and uses in order to optimize the potentials in the breeds. However, most people find it difficult to identify and classify the dog breed groups. Existing works did not consider the automatic grouping of dog breeds. Hence, there is need for automatic techniques to classify dog breeds into groups with improved accuracy. This work used the concept of Convolutional Neural Network (CNN) to develop a model that will automatically classify dog breeds into group based on the American Kennel Club standard using the Stanford’s dog dataset. The developed model achieved 92.2% accuracy, 80.0% sensitivity, 95.3% specificity and 93.4% area under curve (AUC). The model’s performance is excellent compared to existing works that used the same dataset. The experimental result was validated with two classic CNN models (ResNet-50 and SqueezeNet) using the same parameters.
first_indexed 2024-03-11T22:53:49Z
format Article
id doaj.art-c7d6f844327244639d4b524b8b7b2816
institution Directory Open Access Journal
issn 2437-2110
language English
last_indexed 2024-03-11T22:53:49Z
publishDate 2023-09-01
publisher Faculty of Engineering and Technology
record_format Article
series Nigerian Journal of Technological Development
spelling doaj.art-c7d6f844327244639d4b524b8b7b28162023-09-21T21:28:38ZengFaculty of Engineering and TechnologyNigerian Journal of Technological Development2437-21102023-09-01203Automatic classification of Breeds of Dog using Convolutional Neural NetworkP. O. Adejumobi0I. O. Adejumobi1O. A. Adebisi2S. O. Ayanlade I. I. Adeaga3Engr.Ladoke Akintola University, OgbomosoLadoke Akintola University OgbomosoThe Polytechnic Ibadan Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed similarities and intra breed differences thereby creating a difficulty in their classification. The American Kennel Club (AKC) classified breeds of dog into groups based on characteristic, purpose, behaviuor and uses in order to optimize the potentials in the breeds. However, most people find it difficult to identify and classify the dog breed groups. Existing works did not consider the automatic grouping of dog breeds. Hence, there is need for automatic techniques to classify dog breeds into groups with improved accuracy. This work used the concept of Convolutional Neural Network (CNN) to develop a model that will automatically classify dog breeds into group based on the American Kennel Club standard using the Stanford’s dog dataset. The developed model achieved 92.2% accuracy, 80.0% sensitivity, 95.3% specificity and 93.4% area under curve (AUC). The model’s performance is excellent compared to existing works that used the same dataset. The experimental result was validated with two classic CNN models (ResNet-50 and SqueezeNet) using the same parameters. https://journal.njtd.com.ng/index.php/njtd/article/view/1485stanford's dog dataset, convolutional neural networkdeep learning
spellingShingle P. O. Adejumobi
I. O. Adejumobi
O. A. Adebisi
S. O. Ayanlade
I. I. Adeaga
Automatic classification of Breeds of Dog using Convolutional Neural Network
Nigerian Journal of Technological Development
stanford's dog dataset, convolutional neural network
deep learning
title Automatic classification of Breeds of Dog using Convolutional Neural Network
title_full Automatic classification of Breeds of Dog using Convolutional Neural Network
title_fullStr Automatic classification of Breeds of Dog using Convolutional Neural Network
title_full_unstemmed Automatic classification of Breeds of Dog using Convolutional Neural Network
title_short Automatic classification of Breeds of Dog using Convolutional Neural Network
title_sort automatic classification of breeds of dog using convolutional neural network
topic stanford's dog dataset, convolutional neural network
deep learning
url https://journal.njtd.com.ng/index.php/njtd/article/view/1485
work_keys_str_mv AT poadejumobi automaticclassificationofbreedsofdogusingconvolutionalneuralnetwork
AT ioadejumobi automaticclassificationofbreedsofdogusingconvolutionalneuralnetwork
AT oaadebisi automaticclassificationofbreedsofdogusingconvolutionalneuralnetwork
AT soayanlade automaticclassificationofbreedsofdogusingconvolutionalneuralnetwork
AT iiadeaga automaticclassificationofbreedsofdogusingconvolutionalneuralnetwork