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A system that tracks the calories and nutrients in daily meals holds significant potential, particularly given the global surge in interest in healthy eating, weight management, and obesity prevention. With the prevalence of diseases linked to lifestyle choices. These choices make people much prone to health disorders like breast cancer, colon cancer, and type 2 diabetes. The importance of personalized nutrition and health monitoring is increasingly recognized. Achieving a balanced and nutritious diet requires accurate assessment of the nutritional value of foods consumed. In this context, a novel approach to meal calorie estimation utilizing deep learning algorithms emerges as a promising solution. This method involves the analysis of food item images through a deep learning model, specifically the ResNet Architecture, to extract pertinent information crucial for food item identification and calorie content determination. Training the model necessitates a comprehensive dataset encompassing various types of food, ensuring its efficacy and accuracy in providing personalized nutritional guidance.

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Food Calorie Measurement Through Deep Learning ResNet-50 Model

  • 会议论文

  • 发表日期：

    2025年

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  • D. Ratna Kishore,
  • Diyyala Harshith,
  • Venkata Srinadh Reddy Nallapu,
  • Challa Karthik,
  • Prathipati Sathwik

摘要

A system that tracks the calories and nutrients in daily meals holds significant potential, particularly given the global surge in interest in healthy eating, weight management, and obesity prevention. With the prevalence of diseases linked to lifestyle choices. These choices make people much prone to health disorders like breast cancer, colon cancer, and type 2 diabetes. The importance of personalized nutrition and health monitoring is increasingly recognized. Achieving a balanced and nutritious diet requires accurate assessment of the nutritional value of foods consumed. In this context, a novel approach to meal calorie estimation utilizing deep learning algorithms emerges as a promising solution. This method involves the analysis of food item images through a deep learning model, specifically the ResNet Architecture, to extract pertinent information crucial for food item identification and calorie content determination. Training the model necessitates a comprehensive dataset encompassing various types of food, ensuring its efficacy and accuracy in providing personalized nutritional guidance.