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@article{202666,
        author = {Vaishali Sunil Dadas and Chaitali Mahesh Lokhande and Pranoti Anna Shinde},
        title = {Real-Time Food Calorie Detection Using Image Processing CNN-Based Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8769-8775},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202666},
        abstract = {Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. In this paper, we propose a method based on the parameter-optimized Convolutional Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once the identification process of food items is complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process.},
        keywords = {Food image classification, calorie estimation, image recognition, convolutional neural networks, deep learning, real-time systems, MobileNet, Vision Transformer.},
        month = {May},
        }