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A novel deep learning neural network for fast-food image classification and prediction using modified loss function

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Abstract

Accurate food image classification is often critical to accurately monitor the dietary assessment to reduce the risk of different heart-related diseases, obesity, diabetes, and other related health conditions. The accuracy and efficiency of image classification results when using traditional deep learning methods were less than optimal. This research aimed at enhancing the classification and prediction accuracy of food images and reducing the processing time by using the Deep Convolutional Neural Network (DCN) algorithm. The solution starts by using the Modified Loss function, the images are fed into the DCN for features extraction through alternating between convolutional layers and pooling layers, then this is followed by a fully connected layer. Finally, the Softmax function is used to classify the images. The result was compared during the classification phase in the DCN. The proposed solution enhanced the accuracy of the classification by using the regularized loss function and lowered the processing time by decreasing the weights of the neurons in the neural network. Probability score is used as the evaluation metric for the accuracy, and total execution time is used as the evaluation metric for the speed of the algorithm. The combination of deep neural network with regularized cross entropy cost function has improved the fast-food images classification by ahcieving better processing time by 40 ~ 50s and accuracy by 5% in average.

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Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia

    Saurav Lohala, Abeer Alsadoon & P. W. C. Prasad

  2. School of Computer Data and Mathematical Sciences, University of Western Sydney (UWS), Sydney, Australia

    Abeer Alsadoon

  3. Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia

    Abeer Alsadoon

  4. Kent Institute Australia, Information Technology Department, Sydney, Australia

    Abeer Alsadoon

  5. Department of Computer Techniques Engineering, AL Nisour University College, Baghdad, Iraq

    Rasha S. Ali

  6. Mustansiriyah University, College of Science, Department of Computer Science, Baghdad, Iraq

    Alaa Jabbar Altaay

Authors
  1. Saurav Lohala
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  2. Abeer Alsadoon
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  3. P. W. C. Prasad
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  4. Rasha S. Ali
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  5. Alaa Jabbar Altaay
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Correspondence to Abeer Alsadoon.

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Lohala, S., Alsadoon, A., Prasad, P.W.C. et al. A novel deep learning neural network for fast-food image classification and prediction using modified loss function. Multimed Tools Appl 80, 25453–25476 (2021). https://doi.org/10.1007/s11042-021-10916-x

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  • DOI: https://doi.org/10.1007/s11042-021-10916-x

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