Image-Based Food Recognition For Calorie Counting With Convolutional Neural Networks

Abstract

In the pursuit of enhancing dietary tracking, this 
proposal outlines a methodical approach to develop 
an image-based food recognition system utilizing 
Convolutional Neural Networks (CNNs). The core 
objective is to facilitate precise calorie counting 
through automated identification of food items from 
images. Our methodology involves curating a 
comprehensive 
dataset 
of 
food 
images, 
representative of various cuisines and meal 
compositions. This dataset will train a CNN model, 
which is meticulously architected to discern subtle 
textural and shape differences among food items. 
We will employ data augmentation techniques to 
expand the dataset’s diversity, ensuring robustness 
against overfitting and improving the model’s 
generalization capabilities. 
The proposed CNN model will be evaluated through 
rigorous validation processes, employing cross
validation techniques to assess its performance 
across unseen data. We aim to integrate the model 
within a mobile application, providing real-time 
feedback on calorie intake. Additionally, we plan to 
explore transfer learning strategies to adapt the 
model to new food categories efficiently. By 
leveraging state-of-the-art machine learning 
frameworks and cloud computing resources, we 
anticipate a scalable solution that can be deployed 
across different platforms. The outcome of this 
research is expected to contribute significantly to 
personal health management and offer valuable insights for nutritional science and public health 
initiatives.