Scholarlink

This project presents a deep learning-based approach to estimate calories in Arabic food dishes through image analysis. The system integrates a fine-tuned MobileNetV2 model for food recognition and a calorie estimation module that calculates nutritional values based on volume and density. By leveraging transfer learning and advanced preprocessing techniques, the proposed model demonstrates high classification accuracy and reliable calorie prediction. The mobile application interface offers a user-friendly tool for dietary tracking, particularly tailored to Saudi cuisine. This work contributes to digital health efforts aligned with Saudi Vision 2030 by promoting culturally relevant nutrition awareness.

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Study on Arabic Food Calorie Estimation App Using Image Processing

  • 会议论文

  • 发表日期：

    2026年

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  • Mehwash Farooqui,
  • Atta Rahman,
  • Zainab Almahfoudh,
  • Ghadeer Albasha,
  • Amal Alwehibe,
  • Reem Albawardi,
  • Reyam Alrasheed,
  • Amal Alahmadi,
  • May Aldossary

摘要

This project presents a deep learning-based approach to estimate calories in Arabic food dishes through image analysis. The system integrates a fine-tuned MobileNetV2 model for food recognition and a calorie estimation module that calculates nutritional values based on volume and density. By leveraging transfer learning and advanced preprocessing techniques, the proposed model demonstrates high classification accuracy and reliable calorie prediction. The mobile application interface offers a user-friendly tool for dietary tracking, particularly tailored to Saudi cuisine. This work contributes to digital health efforts aligned with Saudi Vision 2030 by promoting culturally relevant nutrition awareness.