Detecting Food Items with Possible Allergens Using Deep Neural Network

Food allergies are a significant concern for the community as they can have adverse effects on human health. Even a small amount of certain food items can trigger allergic reactions in the body, ranging from mild symptoms like hives or itchiness to severe and life-threatening anaphylaxis. However, these reactions can often be prevented by being aware of the specific allergen-based food items and avoiding their consumption. Our research aims to address this issue by utilizing a Convolutional Neural Network-based object. This model, belonging to the realm of deep learning techniques, is utilized to identify food items present in images. The primary objective behind this recognition process is to facilitate the development of a system that can effectively identify ingredients capable of triggering allergic reactions. This study explored three deep learning techniques for the classification of food images. The effectiveness of three distinct deep learning models, namely MobilenetV2, EfficientNetB0 and Resnet50v2, was examined. Notably, the EfficientNetB0 classifier outperformed the others, achieving an impressive accuracy score of 97%.