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 chest X-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.

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Nowadays, almost all applications, especially augmented reality (AR) applications, run on the web. In these applications, determining the position and rotation of the mobile device is an indispensable basic step. The speed and accuracy of this work greatly affect the quality of the user experience. Therefore, estimating the rotational position of mobile devices on the Web is necessary and meaningful in practice. In this paper, we propose a way to estimate the position and rotation of the mobile device using the image of the marker obtained from the camera combined with the data from the device’s tilt angle sensor. The proposal has been experimentally installed and evaluated for performance onWeb Assembly, Java Script, and C++ platforms. Along with that, to ensure objectivity, we compared the speed and calculation error of the proposed technique with the P3P and PnP techniques installed in the OpenCV open-source library. We also use the proposal method to develop the Virtual Museum application for the Vietnam National Museum of Nature

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