Automated Food Prediction Using Deep Learning with Calorie Estimation Algorithm

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@article{1705602,
      author = {R. Samundi, G. S. Gunanidhi, R. Karthikeyan},
      title = {Automated Food Prediction Using Deep Learning with Calorie Estimation Algorithm},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {204-208},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705602.pdf},
      abstract = {Automated food image prediction and classification using deep learning is a popular and powerful approach to categorize and classify food images based on their visual features. Deep learning models, particularly Convolutional Neural Networks (CNNs), have proved effective in multiple visual analysis tasks, including image categorization. This research presents automatic food categorization algorithms based on deep learning methodologies. For food image categorization, SqueezeNet and VGG-19 CNNs were utilised. Automated food image classification in the health and medical field has several potential applications, including dietary analysis, nutritional monitoring, and personalized healthcare. SqueezeNet is a deep neural network architecture specifically designed for efficient model size and computation. VGG-19 known for its deep network architecture and has 19 weight layers, including convolutional and fully connected layers which are able to recognize quite a good accuracy. In Food image classification VGG-19 is get good classification results compare to VGG-16.The classify food item name with images approximately recognition the item. Also introduced food calorie estimation algorithm to predict the calorie content of a food item based on its features or characteristics.},
      keywords = {Automated Food Classification, Deep Learning, SqueezeNet, VGG-19, Calorie Estimation Algorithm.},
      month = {March},
  }