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International Conference on Advanced Intelligent Systems and Informatics

AISI 2018: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018 pp 347–356Cite as

Aquarium Family Fish Species Identification System Using Deep Neural Networks

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 845))

Abstract

In this paper, a system for aquarium family fish species identification is proposed. It identifies eight family fish species along with 191 sub-species. The proposed system is built using deep convolutional neural networks (CNN). It consists of four layers, two convolutional and two fully connected layers. A comparative result is presented against other CNN architectures such as AlexNet and VggNet according to four parameters (number of convolution and fully connected layers, the number of epochs in training phase to achieve 100% accuracy, validation accuracy, and testing accuracy). Through the paper, it is proven that the proposed system has competitive results against the other architectures. It achieved 85.59% testing accuracy while AlexNet achieves 85.41% over untrained benchmark dataset. Moreover, the proposed system has less trained images, less memory, less computational complexity in training, validation, and testing phases.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

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Authors and Affiliations

  1. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha & Aboul Ella Hassanien

  2. Scientific Research Group in Egypt (SRGE), Giza, Egypt

    Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha & Aboul Ella Hassanien

Authors
  1. Nour Eldeen M. Khalifa
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  2. Mohamed Hamed N. Taha
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Nour Eldeen M. Khalifa .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  4. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E. (2019). Aquarium Family Fish Species Identification System Using Deep Neural Networks. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_32

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