An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning

  • Elham Mohammed Thabit A. ALSAADI Computer Science Department, College of Science, University of Kerbala, Kerbala, Iraq
  • Nidhal K. El Abbadi Education College, University of Kufa, Kufa, Iraq
Keywords: Deep Learning, Convolutional Neural Networks, Animals Classification, Mammals, Reptiles

Abstract

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200 images. The accuracy of the system’s prediction for the target object was 97.5%.

Published
2020-09-29
How to Cite
A. ALSAADI, E. M. T., & El Abbadi, N. K. (2020). An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning. Iraqi Journal of Science, 61(9), 2361-2370. https://doi.org/10.24996/ijs.2020.61.9.23
Section
Computer Science