Explainable Artificial Intelligence in Oriental Food Recognition using Convolutional Neural Network

Abstract

Food recognition technology remains a challenging task to the computer vision community due to the diverse nature of food. Oriental food, with similar features such as colour and texture, makes the recognition process less effective and challenging with a convolutional neural network (CNN). More importantly, there are no literature reports on the use of Local Interpretable Model-agnostic Explanations (LIME) to increase the transparency of oriental food recognition. Hence, this paper investigates oriental food recognition using two different CNN models and implements LIME to interpret the model. The testing accuracy obtained by the proposed CNN models for oriental food recognition with optimum hyper-parameter setting is about 85.7% coupled with the utilization of the LIME model to increase the transparency of the deep learning models.