Identify and Analyze the Healthy and Unhealthy Food Items Using Deep Learning

In the recent past, nutrition received wider attention in preventing chronic Diseases. Poor eating habits are the main contributors to health problems globally. It is essential to know and differentiate between healthy and unhealthy food products for a healthier diet. This study introduces a deep learning model that would classify food images as healthy or unhealthy based on their visual attributes. We adopt a CNN-based architecture to feature extraction from images of food products and classify them as healthy or unhealthy. The CNN is trained on a large dataset of labelled food images by using transfer learning techniques to improve the performance. The experimental results clearly show that our proposed method has the ability to categorize food items accurately and with high classification accuracy and robustness to variations in food presentation and image quality. This research contributes to the computer vision and dietary assessment area by offering an automated, scalable solution for assessing healthiness based on visual cues related to food choice. Future work includes extending the dataset with even more varied types of food and deep learning architectures in order to better improve the accuracy of classification.