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Amended Convolutional Neural Network with Global Average Pooling for Image Classification

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

Abstract

Image classification is playing a vital role in several computer vision and pattern recognition applications. Multi-class, corruptions and heterogeneous and complex shapes make the image classification task is extremely challenging. In this article, we introduce a new Convolutional Neural Network (CNN) design that combines several concepts including parallel convolutional layers with different filter sizes and a global average pooling layer (GAP). One of the deep learning limitations is overfitting. To diminish this issue, we have applied a GAP layer at the end of the mode. Different challenging benchmarks are used for evaluation. Specifically, CIFAR-10, CIFAR100, and MNIST are used in our final experiments. We showed that our model surpasses many former models evaluated on the same datasets. It has been proven the proposed model is active in phases of feature extraction and classification.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Al-Nahrain University, Baghdad, Iraq

    Aiman Al-Sabaawi

  2. University of Information Technology and Communications, Baghdad, Iraq

    Hassan M. Ibrahim, Zinah Mohsin Arkah & Laith Alzubaidi

  3. Electrical and Computer Engineering Department, University of Missouri-Columbia, Columbia, MO, USA

    Muthana Al-Amidie

  4. Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Australia

    Laith Alzubaidi

Authors
  1. Aiman Al-Sabaawi
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  2. Hassan M. Ibrahim
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  3. Zinah Mohsin Arkah
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  4. Muthana Al-Amidie
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  5. Laith Alzubaidi
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Corresponding author

Correspondence to Laith Alzubaidi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Al-Sabaawi, A., Ibrahim, H.M., Arkah, Z.M., Al-Amidie, M., Alzubaidi, L. (2021). Amended Convolutional Neural Network with Global Average Pooling for Image Classification. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_16

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