Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Evaluation of two-dimensional approach for computational modelling of heat and momentum transfer in liquid containing horizontal cans and experimental validation

Axi-symmetric approach in vertical cans lead to 2-dimensional (2D) model with shorter computational times. For horizontal cans containing liquids, this approach is not possible due to internally evolved natural convection. If an assumption is made to neglect gravitational effect in horizontal direction, it may be possible to use a 2D model. Therefore, the objective of this study was to evaluate this approach numerically and experimentally. In simulations, continuity, energy and momentum equations were solved in 2D and 3D using Ansys CFX 12.1 for horizontal cans containing water and another viscous liquid. In experiments, water filled cans were prepared, and temperature data were compared with simulation results. The results demonstrated the possible use of 2D approach in modelling for liquid containing horizontal cans with significant reduction in computational times. Optimization algorithms of simulation programs are also expected to be used in combination with simulation studies since computation times are reduced significantly.