Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so early and simple detection of tuberculosis has been more and more important. In our study,we focused on tuberculosis diagnosis by using the chestX-ray image, the essential input for the radiologist's profession, and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images. We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types. We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models. Our experiments were carried out by applying three different architectures, Alexnet, Resnet, and Densenet, on international, Vietnamese, and combined X-ray image datasets. After training, all models were verified on a pure Vietnamese X-rays set. The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC (Area under the Receiver Operating Characteristic Curve), sensitivity, specificity, and accuracy. In the best strategy, most of the scores were more than 0.93, and all AUCs were more than 0.98. © 2023 Tech Science Press. All rights reserved.

A Framework for Masked-Image Recognition System in COVID-19 Era

Face Recognition techniques have been widely developed and used for many years. Several approaches and models are adopted and successfully used to perform face recognition in airports, supermarkets, banks, etc. However, with the emergence of the COVID-19 pandemic, the whole world came across the requirement to use face masks. The mask’s partial covering of the face makes some well-known face recognition algorithms perform poorly or even fail. This paper has developed a real-time framework to detect, recognize, and identify people to authenticate them before accessing an app, device, or location. The newly created framework offers a unique set of capabilities, including the ability for users to select from various authentication methods based on their preferences or circumstances. The application’s face recognition section uses cutting-edge AI and computer vision algorithms to offer the user accurate face detection and recognition, even when the face is partially hidden behind a mask. © 2022, Springer Nature Switzerland AG.