  Food classification is an important task in health care. In this work, we
propose a multimodal classification framework that uses the modified version of
EfficientNet with the Mish activation function for image classification, and
the traditional BERT transformer-based network is used for text classification.
The proposed network and the other state-of-the-art methods are evaluated on a
large open-source dataset, UPMC Food-101. The experimental results show that
the proposed network outperforms the other methods, a significant difference of
11.57% and 6.34% in accuracy is observed for image and text classification,
respectively, when compared with the second-best performing method. We also
compared the performance in terms of accuracy, precision, and recall for text
classification using both machine learning and deep learning-based models. The
comparative analysis from the prediction results of both images and text
demonstrated the efficiency and robustness of the proposed approach.
