Food Allergen Detection in Malaysian Food Using Convolutional Neural Networks

Food allergy is a rising, global epidemic. Some Malaysian cooking contains food-allergic-reaction-causing ingredients that may cause severe allergic reactions. A food allergen detection system in Malaysian food is proposed for tourists with food allergies who are unfamiliar with the wide variety of Malaysian dishes to prevent severe allergic reactions. This work focuses on three major food allergens, which include peanuts, cow’s milk, and shellfish. A new Malaysian food image dataset was prepared, and transfer learning on the custom dataset was done via fine-tuning and feature extraction techniques. Comparisons on the ResNet50, InceptionV3, and VGG16 architectures are done based on the accuracy of each model on the testing data. The VGG16 architecture is concluded as the most suitable neural network model for food allergen detection in Malaysian food. The proposed classifier achieved an accuracy of 80.56% on the test samples. The final model is loaded into a Graphical User Interface (GUI) application to demonstrate the results of the Malaysian food classification model.