@inproceedings{a118aea27ac94bec80f805a57c4ccf1e,
title = "Study on Arabic Food Calorie Estimation App Using Image Processing",
abstract = "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.",
keywords = "Calorie Estimation, Deep Learning, Food Recognition, Image Segmentation, MobileNetV2, Saudi Cuisine, Transfer Learning",
author = "Mehwash Farooqui and Atta Rahman and Zainab Almahfoudh and Ghadeer Albasha and Amal Alwehibe and Reem Albawardi and Reyam Alrasheed and Amal Alahmadi and May Aldossary",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on the AI Revolution: Research, Ethics, and Society, AIR-RES 2025 ; Conference date: 14-04-2025 Through 16-04-2025",
year = "2026",
doi = "10.1007/978-3-032-13056-3\_15",
language = "English",
isbn = "9783032130556",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "190--205",
editor = "Arabnia, \{Hamid R.\} and Leonidas Deligiannidis and Soheyla Amirian and \{Ghareh Mohammadi\}, Farid and Farzan Shenavarmasouleh",
booktitle = "AI Revolution",
}