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Free, publicly-accessible full text available May 1, 2025
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Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we propose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then we discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also incorporated the improvement of convolution kernel size, feature dimension, and network depth. Thirdly, the effects of different pooling methods, activation functions and training algorithms we proposed has been analyzed to demonstrate the advantages of the new strategy. Finally, the experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules, and the evaluation shown that our proposed method could achieve an outstanding results compare to state-of-arts.more » « less
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Cybersickness in Virtual Reality (VR) is a serious issue affecting the overall experience. Many research papers have investigated the causes of cybersickness and offered potential solutions for reducing cybersickness. In this paper, we demonstrate a method to reduce cybersickness by using a novel rendering technique in the virtual environment (VE)- Dynamic Mono-Stereoscopic Rendering System (DMSRS). The DMSRS system uses two different cameras to create a hybrid rendering that includes monoscopic and stereoscopic systems. By default, VEs are rendered using stereoscopic or monoscopic rendering exclusively. The results indicate that cybersickness decreased amongst users with little to no VR experience hindered when using the DMSRS.more » « less