An Efficient Deep Convolutional Neural Network for Visual Image Classification

  • Basma Abd El-Rahiem
  • Muhammad Atta Othman AhmedEmail author
  • Omar Reyad
  • Hani Abd El-Rahaman
  • Mohamed Amin
  • Fathi Abd El-Samie
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Such a hot open issue in the area of computer vision is the classification of visual images especially in Internet of Things (IoT) and remote mid-band and high-band based connections. In this paper, we propose a robust and efficient taxonomy framework. The proposed model utilizes the well-known convolutional neural network composites to construct a robust Visual Image Classification Network (VICNet). The VICNet consists of three convolutional layers, four Relu/Leaky Relu activation layers, three max-pooling layers and only two fully connected layers for extracting expected input image features. To make the training process faster, we used non-saturating neurons with a very efficient Graphics Processing Unit (GPU) implementation for the convolution operation. To minimize over-fitting issue in the fully-connected layers, we utilized a recently-developed regularization approach “dropout” with a dropping probability of 50%. The proposed VICNet framework has a high potential capability in the recognition of test images. The experimental and simulations results proven the efficacy of the proposed model.


Deep learning Visual image classification Convolutional neural network Deep feature extraction Transfer learning 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of ScienceMenoufia UniversityShibin El KomEgypt
  2. 2.Faculty of Computers and InformationSouth Valley UniversityLuxorEgypt
  3. 3.Faculty of ScienceSohag UniversitySohagEgypt
  4. 4.Faculty of Electronic EngineeringMenoufia UniversityShibin El KomEgypt

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