Food Image Generation and Appetite Evaluation Based on Appetite Factor Analysis by Image Features

In recent years, due to the COVID-19 pandemic and the widespread use of technology, the Internet and food and beverage websites are often used for take-out and food and beverage reservations, and information such as reviews and photos on these platforms has a significant impact on revenue. In this study, to develop an appetite-enhancing application, we focus on food images that strongly influences appetite and analyze what image features stimulate appetite. Then, based on the results of the analysis, we generate appetizing images using GAN (Generative Adversarial Network). © 2022 SPIE.

COVID-19 Face Mask Restoration Using Pix2pix model

Accurate facial recognition can effectively help the population combat the disease by offering risk-free phone usage, access controls, etc. In the era of COVID-19, a mask has become a necessity. However, masks may reduce the accuracy of face recognition to some degree. Thus, it is necessary to use deep learning to increase face recognition accuracy by recovering the face with a mask. For this purpose, this study proposed an AI-based model based on Pix2pix and U-net generator for restoring face mask images using the paired image database. In the training step, we used two adversarial models, including one generator and one discriminator. Then they are extended to a conditional model, which will be piped to the Pix2pix algorithm once again. U-Net was built in the training of the generator. The loss curves of generator and discriminators show that as iteration time increases, the loss of fake discriminator becomes lower stably. In contrast, the loss of real discriminator has the same tendency. In the meantime, the loss of generator shows an increased tendency. The result indicates that our model can help build reliable face mask restoration for daily use, which helps to improve the recognition accuracy of the face with a mask.

P2P-COVID-GAN: Classification and Segmentation of COVID-19 Lung Infections From CT Images Using GAN

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.