COVID-19 Detection Using Covolutional Neural Network

The increasing number of cases of confirmed coronavirus disease (COVID-19) in China is striking. The purpose of this study was to investigate the relation between chest CT findings and the clinical conditions of COVID-19 pneumonia. Among those who develop symptoms, most (about 80%) recover from the disease without needing hospital treatment. About 15% become seriously ill and require oxygen and 5% become critically ill and need intensive care. Complications leading to death may include respiratory failure, acute respiratory distress syndrome (ARDS), sepsis and septic shock, thromboembolism, and/or multiorgan failure, including injury of the heart, liver or kidneys. In rare situations, children can develop a severe inflammatory syndrome a few weeks after infection. Proposed method not only detects the availability of NOVEL CORONA but also it tracks the treatment progress. In Second generation, number of architectures or algorithms is present for classification problem. In other languages we have to start from scratch, but for MATLAB and Python this is another case. Simply calling those functions and changing the input argument, you test. Due to available built in commands, design and development time get reduced. With minimal Mathematics behind deep learning, we can design and test various architectures of neural network.