DNCS: Artificial Intelligence Enabled Food Recognition and Associated Calorie Level Estimation System Using Deep Neural Classification Scheme

The DNCS (Cascaded CNN-ANN) enabled Food Recognition (FR) and Associated Calorie Level Estimation System presents a novel approach to food classification and calorie estimation using a cascaded neural network architecture. The system combines Convolutional Neural Networks (CNN) for food image recognition with Artificial Neural Networks (ANN) for calorie estimation, leveraging fine-tuned pre-trained models such as InceptionV3 and EfficientNet. The system demonstrated high FR accuracy across various datasets, achieving a peak accuracy of 96.2% for common food items like burgers and pizzas, with an overall accuracy range between 89.2% and 96.2%. For calorie estimation, the system achieved a Mean Absolute Error (MAE) of less than 6.1%, demonstrating reliability in predicting calorie content based on food portion size. The use of image segmentation techniques like graph-cut and GrabCut further enhanced the accuracy of FR. The system was tested for real-time applications, achieving an average processing time of 350 to 450 milliseconds per image, making it suitable for integration into mobile applications. User feedback highlighted ease of use, speed, and accuracy as key strengths, with an overall satisfaction rating of 4.7 out of 5. These results suggest that the DNCS system offers an efficient solution for dietary management through real-time calorie tracking.